Post on 05-Oct-2020
Chapter 6 CLASSIFICATION ALGORITHMS FOR DETECTION OF
ABNORMALITIES IN MAMMOGRAM IMAGES
The two deciding factors of an efficient system for the detection of abnormalities are
the nature and type of features extracted and the classifier employed. In this chapter
we carried out a detailed study of the suitability of two chosen feature sets GLCM
and Wavelet coefficients. For the experiments, we selected distance measure,
Multilayer Perceptron (MLP), Extreme Learning Machine (ELM) and group of
Lazy classifiers. Standard database is used for all experiments. Performances of the
systems are measured with class accuracy, Sensitivity and Specificity. The result
obtained by the different classifiers against each feature set is compared.
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130 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
6.1Introduction
The human beings are the best pattern recognizers. But we do not
know how the brain understand and recognize patterns. Pattern recognition is
the study of how machines can observe the environment, learn to distinguish
pattern of interest from their background, and make sound and reasonable
decisions about the categories of the patterns. Automatic (machine)
recognition, description, classification and grouping of patterns are important
problems in a variety of engineering and scientific disciplines. Pattern
recognition can be viewed as the categorization of input data into identifiable
classes via the extraction of significant features or attributes of the data. Duda
and Hart [Duda and Hart 1973], [Duda et.al. 2001] define pattern recognition
as a field concerned with machine recognition of meaningful regularities in
noisy or complex environment. It encompasses a wide range of information
processing problems of great practical significance from pattern recognition
of simple patterns to complex patterns like breast tumor detection in medical
diagnosis. Today, pattern recognition is an integral part of the intelligent
systems built for decision making. Normally the pattern recognition
processes make use of one of the following two classification strategies.
i. Supervised classification in which the input patterns are identified as a
member of a predefined class.
ii. Unsupervised classification in which the patterns are assigned to a
hitherto unknown class.
In this chapter we focus on the classification of mammogram images
using different supervised classification techniques such as simple distance
measure, ANN, ELM and Lazy classifiers. Unsupervised classification
techniques like clustering algorithms are dealt in the next chapter.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 131
For building successful classifiers, we have to define appropriate
features capable of characterizing the image features. In this work two
effective approaches are employed for feature extraction. In the first approach
wavelet transformation is employed to extract features. A set of high valued
wavelet coefficients selected from the ‘approximation band’ form the feature
vector. Experiments are conducted with two prominent wavelet
decomposition schemes viz. Stationary Wavelet Transformations (SWT) and
Discrete Wavelet Transformations. In each case representatives from
different wavelet filter families are employed. In the second approach, texture
features extracted using GLCM form the basis of classification. The feature
vector is formed with contrast, energy, homogeneity and correlation values
extracted from GLCM.
A wide range of classifiers are chosen for the study. Classification
experiments with the different features are carried out with distance measure,
Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and a set
of lazy classifiers. A systematic analysis of performance of the different
feature set-classifier pair is carried out by employing the chosen dataset
explained in section 3.2. Further for ANN, ELM and Lazy classifiers a
feature reduction method is implemented to reduce the complexity. In the
next session, a comprehensive review of related works is presented. This is
followed by the description of the experiments carried out and detailed
analysis of the results obtained.
6.2 Related Work
Ferreira and Borges [Ferreira and Borges, 2003] proposed and
implemented a supervised classification algorithm for classification of radial,
circumscribed, microcalcifications, and normal samples of mammogram
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132 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
images using wavelet transformations. The authors also used a special set of
coefficients as features and Euclidean distance for separating mammogram
images into benign, malignant and normal.
Soltanian-Zadeh et.al [Soltanian-Zadeh et.al, 2004] presented an
evaluation of the performance of four different texture and shape feature
extraction methods for the classification of benign and malignant micro
calcifications in mammograms. They extracted microcalcification clusters,
texture and shape features using different approaches like conventional shape
quantifiers, co-occurrence based method of Haralick [Soltanian-Zadeh et.al,
2004] and multi level wavelet transformations.
Rashed and Awad [Rashed and Awad, 2006] developed a supervised
diagnosis system for digital mammograms. In this model, a diagnosis process
is done by transforming the image data into feature vectors using wavelets
multilevel decomposition. This vector is used as the features for separating
different mammogram classes. This model classified mammogram images
into tumor types and risk level. The result reported is very encouraging.
Rashed et al [Rashed et al, 2007] also suggested a multiresolution
analysis system for interpreting digital mammograms. This system is based
on using fractional amount of biggest wavelets coefficients in multilevel
decomposition. They used 25% of the Mini-MIAS images for creating a class
core vector and the entire ROI’s of mammogram images from Mini-MIAS
database is classified by taking the minimum Euclidean distance measure
from each mammogram images to the class core vector.
A comparative study made by the Nithya and Santhi [Nithya and
Santhi, 2011a] on the second order statistical feature extraction methods
shows significant results compared to other methods. The study used a
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 133
sample of 50 mammogram images from the DDSM database. The same
authors [Nithya and Santhi, 2011b] proposed another method incorporating
GLCM features and ANN for the classification of normal and abnormal
patterns in digital mammograms and reported sensitivity and specificity more
than 90% for a sample set of 50 digital mammogram images from the DDSM
dataset.
[Khuzi et.al, 2009] proposed a method for the detection and
classification of masses and non-masses in a mammogram images using
GLCM features. This method extracted the features from the ROIs which
were segmented using algorithms such as Otsu thresholding and K-means.
The accuracy of the classification is measured with a sample set consisting of
20 abnormal and 20 normal images from the Mini-MIAS database. The work
reported an accuracy of more that 80% for both Otsu thresholding techniques
and 70% for K-Means.
A hybrid feature reduction method namely Linear forward selection
and genetic algorithm for reducing the GLCM feature sets was proposed by
Vasantha and Bharathi [Vasantha and Bharathi, 2010] [Vasantha and
Bharathi, 2011]. In this work 60 images from DDSM database and 118
images from Mini-MIAS database were used with decision tree classifier.
They could achieve 86% accuracy with DDSM and 95% with Mini-MIAS
Database.
Using ANN and GLCM features, Abdulla et.al [Abdulla et.al, 2011]
proposed a method for detection of masses in digital mammogram and
achieved 91% sensitivity and 84% specificity while classifying 90
mammogram images randomly selected from the Mini-MIAS database. Islam
et al. [Islam et.al, 2010] also proposed a classification method using ANN
Chapter 6
134 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
and GLCM features to classify benign-malignant classes of mammogram
images and achieved 90% sensitivity and 84% specificity.
6.3 Detection and Classification of Mammogram Images Using
Different Distance Measures
One of the simplest approach for classification is by employing a
distance measure. The general principle of such a classification is that for
each class in the domain of interest, a class core vector is formed by using the
features extracted from a set of representative images from the class. If Ck is
the class core vector of kth class and F is the feature vector extracted from a
test image I, then I ∈ k if dist(F, Ck) is minimum for some distance measures.
In this section we discuss the classification experiments carried out with two
prominent distance measures – Euclidean and Bray Curtis. Both wavelet
feature and GLCM features are considered for the experiments.
6.3.1 Classification of Mammogram Images Using Wavelet
Transformation Features
Image texture is a confusing measurement that depends mainly on the
scale in which the data are observed. Different types of images have different
types of textures. Textures of mammograms are irregular and it posses fuzzy
like characteristics. Wavelet transformation is the best tool for analyzing
images of these characteristics. We propose a modified version of the works
proposed in [Ferreria and Borges, 2003] and [Rashed et.al, 2007] for
classifying mammogram images using wavelet multiresolution analysis.
SWT and DWT of an image result in a set of wavelet coefficients at different
levels of decomposition. Of these, the approximation coefficients set is found
to characterize the texture properties of the image. A subset from each level
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 135
of transformation comprising of a predefined fraction of the biggest
coefficients in that level is selected for forming feature vector.
In the proposed approach, Region of Interest (ROIs) of size 124x124
is extracted from each mammogram images in the dataset. Each ROI in the
dataset is subjected to wavelet decomposition using different types of wavelet
filters. The decomposition is carried out up to 4 levels. For each class m, four
class core vectors jmC are formed corresponding to the four levels of
decomposition using the equation 6.1
)i(AN1 C
N
1i
jm
jm ∑
=
= (6.1)
Where j = 1, 2, 3 & 4, the number of levels of the wavelet decomposition, N
is the total number of ROIs in a classes m of the images in the dataset. jmA is
the feature vector containing α % of the wavelet coefficients in level j of the
transformed ROI belonging to class m and α is a predefined value.
In order to classify a mammogram images, we extracted a set of 322
ROIs of size 124 x 124 pixels from the 322 mammogram image available in
the Mini-MIAS database by identifying the center location of the abnormality
of the mammogram images. The extracted ROIs contain benign, malignant
and normal images. The class core vector for the classes normal, benign and
malignant, are created by taking only 10% of the ROIs randomly selected
from each class as the training set. The classification of new instance (ROI) is
carried out by defining a distance measure Dist (A, m) as the distance of A
from a class m, given by equation 6.2
)C - A(dj1 )m,A(Dist l
m
j
1l
l∑=
= (6.2)
Chapter 6
136 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
where A is the feature vector of the test image, Cm is the class core vector, j is
the number of decomposition levels, m represents the number of classes in
the image set and Dist(.) depends on the distance function used. With
Euclidean distance measure lm
l C - A(d ) is defined as:
∑=
−=−q
1i
2lm
llm
l ))i(C)i((A )C A(d (6.3)
where )i(A i is the ith coefficient value in the feature vector of jA , )i(C lm is
the ith coefficient value in the lmC , q is the number of wavelet transformation
coefficients used that depends on α and l denotes the level of the wavelet
decomposition. To study the influence of the type of wavelet transformations,
we conducted experiment with SWT and DWT. Also to compare the
performance with different family of wavelet, we employed representatives
from Daubechies, Haar and Biorthogonal wavelets. To study the impact of
distance measure on classification performance, another distance measure
Bray Curtis [Faith et.al, 1987] [Kadir et.al, 2012] defined by equation 6.4 is
also used.
|)C A(|dj1)m,A(Dist l
ml −= ∑ (6.4)
and |)C - A(|d lm
l can be defined as
∑∑
∑=
=
+
−=
j
1i lm
l
lm
q
1k
l
lm
l
|)i(C )i(A|
|)i(C )i(A|
j1 )C - A(|d (6.5)
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 137
6.3.2 Results and Discussion
We implemented the above algorithms in MATLAB and tested the
performance of the algorithm using a dataset consisting of three different
classes of images namely Normal (N), Benign (B) and Malignant (M). In
order to extract wavelet coefficients features, we have used two different
types of wavelet transformations viz. Stationary Wavelet Transformation and
Discrete Wavelet Transformations. The class core vector is created by taking
10% of mammogram images randomly from each class of images in the
dataset. For testing we have chosen 162 mammogram ROIs randomly
selected from the dataset which comprises of 98 normal images, 38 benign
and 26 malignant images.
The wavelet coefficients are generated using three wavelets families.
The filters used in each family are the Daubechies-4, Daubechies-8 and
Daubechies-16 from Daubechies family, Haar wavelet from Haar family and
Bior2.8 from the Biorthogonal family. For both SWT and DWT, four levels
of decomposition are applied resulting in four sets of wavelet coefficients.
Experiment is conducted with different values of α (the fraction of wavelet
transformation coefficients chosen). The distance measures namely Euclidean
and Bray Curtis are used for calculating the distance between the class core
vector and the feature vector of the test images. A class label is attached to
each test image based on the minimum distance criteria.
6.3.2.1 Performance Analysis of SWT Features
The classification results of 162 mammogram ROIs using Stationary
Wavelet Transformation with the two different distance measures are given
in Table 6.1 to Table 6.4. These tables show the confusion matrices generated
during the classification of images.
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138 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Table 6.1: Classification of mammogram images using Daubechies filters in SWT (Euclidean)
Coef. In %
Daubechies Db4 Db8 Db16
25
N B M T N B M T N B M T N 77 11 10 98 77 11 10 98 78 10 10 98 B 12 26 0 38 11 27 0 38 09 29 0 38 M 08 03 15 26 08 03 15 26 08 03 15 26 T 97 40 25 162 96 41 25 162 95 42 25 162
50
N 76 12 10 98 78 10 10 98 78 10 10 98 B 11 27 0 38 11 27 0 38 10 28 0 38 M 07 03 16 26 07 03 16 26 07 03 16 26 T 94 42 26 162 96 40 26 162 95 41 26 162
75
N 77 11 10 98 77 11 10 98 78 10 10 98 B 11 27 0 38 11 27 0 38 10 28 0 38 M 07 03 16 26 07 03 16 26 07 03 16 26 T 95 41 26 162 95 41 26 162 95 41 26 162
100
N 78 10 10 98 78 10 10 98 77 11 10 98 B 11 27 0 38 11 27 0 38 10 28 0 38 M 07 03 16 26 07 03 16 26 06 03 17 26 96 40 26 162 96 40 26 162 93 42 27 162
N: Normal image B: Benign image M: Malignant image T: Total
Table 6.2: Classification of mammogram images using Haar and Biorthogonal filters in SWT (Euclidean)
Cof. In % Haar Biorthogonal
25
N B M T N B M T N 76 11 11 98 78 11 09 98 B 12 26 0 38 08 30 0 38 M 08 02 16 26 08 03 15 26 T 96 39 27 162 94 44 24 162
50
N 78 09 11 98 77 10 11 98 B 12 26 0 38 10 28 0 38 M 07 03 16 26 07 03 16 26 T 97 38 27 162 94 41 27 162
75
N 78 09 11 98 77 11 10 98 B 11 27 0 38 10 28 0 38 M 07 03 16 26 07 03 16 26 T 96 39 27 162 94 42 26 162
100
N 78 09 11 98 78 10 10 98 B 11 27 0 38 11 27 0 38 M 07 03 16 26 07 03 16 26 T 96 39 27 162 96 40 26 162
N: Normal image B: Benign image M: Malignant image T: Total
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 139
Table 6.3: Classification of mammogram images using Daubechies filters in SWT (Bray Curtis)
Coef. In %
Daubechies Db4 Db8 Db16
25
N B M T N B M T N B M T N 73 13 12 98 74 13 11 98 77 10 11 98 B 08 26 04 38 08 28 02 38 07 29 02 38 M 06 04 16 26 06 04 16 26 06 04 16 26 T 87 43 32 162 88 45 29 162 90 43 29 162
50
N 73 13 12 98 74 12 12 98 77 10 11 98 B 07 23 08 38 08 27 03 38 07 29 02 38 M 06 04 16 26 06 04 16 26 06 04 16 26 T 86 40 36 162 88 43 31 162 90 43 29 162
75
N 73 13 12 98 74 13 11 98 76 11 11 98 B 10 21 07 38 08 28 02 38 07 28 03 38 M 06 04 16 26 06 04 16 26 06 04 16 26 T 89 38 35 162 88 45 39 162 89 43 30 162
100
N 74 13 11 98 74 13 11 98 76 11 11 98 B 10 22 06 38 08 26 04 38 07 28 03 38 M 06 04 16 26 06 04 16 26 06 04 16 26 T 90 39 33 162 88 43 31 162 89 43 30 162
N: Normal B: Benign M: Malignant T: Total
Table 6.4: Classification of mammogram images using Haar and Biorthogonal Filters in SWT (Bray Curtis)
Coef. In % Haar Biorthogonal
25
N B M T N B M T N 73 13 12 98 75 12 11 98 B 18 12 08 38 07 29 02 38 M 05 04 17 26 06 04 16 26 T 96 29 37 162 98 45 29 162
50
N 73 13 12 98 75 12 11 98 B 18 12 08 38 08 28 02 38 M 05 04 17 26 06 04 16 26 T 96 29 37 162 99 44 29 162
75
N 73 13 12 98 75 12 11 98 B 18 11 09 38 08 28 02 38 M 05 04 17 26 06 04 16 26 T 96 28 38 162 99 44 29 162
100
N 73 13 12 98 75 12 11 98 B 18 12 08 38 08 26 04 38 M 05 04 17 26 06 04 16 26 T 96 29 37 162 99 42 31 162
N: Normal B: Benign M: Malignant T:total
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140 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Based on the above tables, the performance of the classification
algorithms using different wavelet families, different distance measures and
different α(% of wavelet transformation coefficients) in SWT are evaluated
and they are shown in the Table 6.5 to Table 6.8. In addition to this, we also
evaluated the performance of the classifiers using two important parameters
Sensitivity and Specificity defined in chapter 3 based on the risk level of the
classification. The above classification algorithm classifies mammogram
ROIs into Normal, Benign and Malignant type. Out of these three classes
malignant images pose more risk (cancerous) and need further investigation.
Benign types are non cancerous and can be treated as normal mammogram
images. Based on this, we evaluated Specificity and Sensitivity of the SWT
based classification algorithm employing Euclidean and Bray Curtis measure
as shown in Table 6.9 and 6.10.
Table 6.5: Successful classification rate (in %) of mammogram images using discrete stationary Daubechies filters (Euclidean)
Coef In %
Db4 Db8 Db16
N B M Performance N B M Perfor
mance N B M Performance
25
50
75
100
78.57
77.55
78.55
79.59
68.42
71.05
71.05
71.05
57.69
61.54
61.54
61.54
72.84
73.46
74.07
74.69
78.57
79.59
78.57
79.59
71.05
71.05
71.05
71.05
57.69
61.54
61.54
61.54
73.46
74.69
74.07
74.69
79.59
79.59
79.59
78.57
76.32
73.68
73.68
73.68
57.69
61.54
61.54
65.38
75.31
75.31
75.31
75.31
N : Normal B: Benign M:Malignant
Table 6.6: Successful classification rate (in %) of mammogram images using discrete
stationary Haar and Biorthogonal filters (Euclidean)
Coef. In %
Haar Biorthogonal N B M Performance N B M Performance
25 50 75 100
77.55 79.59 79.59 79.59
68.42 68.42 71.05 71.05
61.54 61.54 61.54 61.54
72.84 74.07 74.69 74.69
79.59 78.57 78.57 79.59
78.95 73.68 73.68 71.05
57.69 61.54 61.54 61.54
75.93 74.69 74.69 74.69
N : Normal B: Benign M:Malignant
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 141
Table 6.7: Successful classification rate (in %) of mammogram images using discrete stationary Daubechies filters (Bray Curtis)
Coef. In %
Db4 Db8 Db16
N B M Performance N B M Perfor
mance N B M Performance
25
50
75
100
74.49
74.49
74.49
75.51
68.42
60.53
55.26
57.89
61.54
61.54
61.54
61.54
70.99
69.14
67.90
69.14
75.51
75.51
75.51
75.51
73.68
71.05
73.68
68.42
61.54
61.54
61.54
61.54
72.84
72.22
72.84
71.60
78.57
78.57
77.55
77.55
76.32
76.32
73.68
73.68
61.54
61.54
61.54
61.54
75.31
75.31
74.07
74.07
N: Normal B: Benign M: Malignant
Table 6.8: Successful classification rate (in %) of mammogram images using discrete stationary Haar and Biorthogonal filters (Bray Curtis)
Coef. In %
Haar Biorthogonal N B M Performance N B M Performance
25
50
75
100
74.49
74.49
74.49
74.49
31.58
31.58
28.94
31.58
65.38
65.38
65.38
65.38
62.96
62.96
62.35
62.96
76.53
76.53
76.53
76.53
76.32
73.68
73.68
68.42
61.54
61.54
61.54
61.54
74.07
73.46
73.46
72.22
N: Normal B: Benign M: Malignant
Table 6.9: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in SWT (Euclidean)
Coef. In %
Wavelet Filter: db4
Wavelet Filter: db8
Wavelet Filter: db16
Wavelet Filter: Haar
Wavelet Filter: Biorthogonal
SN SP SN SP SN SP SN SP SN SP 25
50
75
100
57.69
61.54
61.54
61.54
92.65
92.65
92.65
92.65
57.69
61.54
61.54
61.54
92.65
92.65
92.65
92.65
57.69
61.54
61.54
61.54
92.65
92.65
92.65
92.65
61.54
61.54
61.54
61.54
91.91
91.91
91.91
91.91
57.69
61.54
61.54
61.54
93.38
91.91
91.91
91.91
SN = Sensitivity SP = Specificity
Table 6.10: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in SWT (Bray Curtis)
Coef In %
Wavelet Filter: Db4
Wavelet Filter: Db8
Wavelet Filter: Db16
Wavelet Filter: Haar
Wavelet Filter: Biorthogonal
SN SP SN SP SN SP SN SP SN SP 25 50 75 100
61.54 61.54 61.54 61.54
88.24 85.29 86.03 87.50
61.54 61.54 61.54 61.54
90.44 88.97 90.44 88.97
61.54 61.54 61.54 61.54
90.44 90.44 89.71 89.71
65.38 65.38 65.38 65.38
85.29 85.29 84.56 84.56
61.54 61.54 61.54 61.54
90.44 90.44 90.44 88.97
SN = Sensitivity SP=Specificity
Chapter 6
142 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
From the above tables, a summary of the performance of the
classifiers is given below.
i) Euclidean distance measure with α = 25%
The overall performance of the classification is 75.93% in
Biorthogonal filter followed by 75.31% in db16 wavelet filter.
Out of 98 normal mammogram images, 78 (79.59%) images are
correctly classified using Biorthogonal and db16 wavelets.
Out of 38 benign images 30 (78.95%) images are correctly classified
using the Biorthogonal wavelet filter.
Out of 26 malignant images 16 (61.54%) images are correctly
identified and labeled by the Haar wavelet filter whereas it is 57.69
% for all other wavelet filters.
The optimum sensitivity is obtained in Haar wavelet filter with 61.54
% followed by all other wavelet filters with sensitivity 57.69%.
The highest specificity (93.38%) is obtained in Biorthogonal wavelet
filter, which is followed by all Daubechies filters with 92.65%.
ii) Euclidean distance measure with α = 50%
The overall performance of the classification is 75.31% obtained with
db16 filter.
Out of 98 normal mammogram images, 78 (79.59%) images are
correctly classified using db8, db16 and Haar wavelet filter
Out 38 benign images 28 (73.68%) images are correctly classified
using db16 and Biorthogonal wavelet filter.
Out of 26 malignant images 16 (61.54%) images are classified
correctly using four wavelet filters.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 143
The sensitivity is 61.54% for all wavelet filters used in the
experiment.
The optimum specificity (92.65%) is obtained for all Daubechies filters
followed by specificity (91.91%) for Haar and Biorthogonal filters.
The performance of the different wavelet filters with different α are
shown in Figure 6.1 to 6.4.
Figure 6.1: Classification performance of mammogram images using 25% wavelet transofrmation
coefficients ( Euclidean distance)
Figure 6.2: Classification performance of mammogram images using 50% wavelet transformation
coefficients (Euclidean Distance)
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
Chapter 6
144 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.3: Classification performance of mammogram images using 75% wavelet transformation
coefficients (Euclidean Distance)
Figure 6.4: Classification performance of mammogram images using 100% wavelet
transformation coefficients (Euclidean Distance)
iii) Bray Curtis distance measure with α = 25%
The overall performance of the classification is 75.31% obtained with
db16 wavelet filter.
Out of 98 normal mammogram images, db16 wavelet filter classified
77 (78.57 %) images correctly.
29 (76.32%) benign images are identified by db16 wavelet filter from
the 38 benign images in the dataset.
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
benign
Malignant
Overall
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sifica
tion
perf
orm
ance
in%
Normal
Benign
Malignant
Overall
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 145
Using Haar wavelet filter, out of 26 malignant images, 16 (65.38%)
images are correctly classified.
The highest Sensitivity value obtained is 65.38% for Haar wavelet
filter.
The highest Specificity value is 90.44% for wavelet filters such as
db8, db16 and Biorthogonal.
iv) Bray Curtis distance measure with α = 50%
The overall performance of the classification is 75.31% obtained with
db16 wavelet Filter.
Out of 98 normal mammogram images, db16 wavelet filter classified
77 (78.57%) correctly.
29 (76.32%) benign images are identified by db16 wavelet filter from
the 38 benign images.
Using Haar wavelet filter, out of 26 malignant images, 16 (65.38%)
images are correctly classified.
Optimum sensitivity value obtained is 65.38% for Haar wavelet filter
and specificity 90.44% for db16 and Biorthogonal filters.
The result obtained establishes the fact that increasing ‘α’ beyond a
limit will not improve the performance. The limit is to be found empirically.
Smaller ‘α’ results compact feature vector and hence the classification
process becomes faster. The overall performance of the classification using
different wavelet family with different α and Bray Curtis distance measures
are shown in Figure 6.5 to 6.8.
Chapter 6
146 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.5: Classification performance of mammogram images using 25% wavelet transofrmation coefficients ( Bray Curtis distance)
Figure 6.6: Classification performance of mammogram images using 50% wavelet transformation
coefficients (Bray Curtis Distance)
Figure 6.7: Classification performance of mammogram images using 75% wavelet transformation
coefficients (Bray Curtis Distance)
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
overall
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
sific
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rfor
man
ce in
%
Normal
Benign
Malignant
overall
0102030405060708090
db4 db8 db16 Haar Biorthogonal
Clas
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atio
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ce in
%
Normal
Benign
Malignant
overall
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 147
Figure 6.8: Classification performance of mammogram images using 100% wavelet transformation
coefficients (Bray Curtis Distance)
Out of the different filters used in SWT, Biorthogonal and db16 found
to outperform others in terms of overall classification accuracy. When it
comes to the accurate classification of malignant types, Haar filter has an
edge over others. In general Euclidean distance measure performed slightly
better than Bray Curtis.
6.3.2.2 Performance Analysis of DWT Features
The classification results of 162 mammogram ROIs using Discrete
Wavelet Transformation (DWT) and the two different distance measures are
given in Table 6.11 to Table 6.14.
0
10
20
30
40
50
60
70
80
90
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
overall
Chapter 6
148 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Table 6.11: Classification of mammogram images using Daubechies filter in DWT (Euclidean)
Coef. in %
Wavelet Filter : Daubechies db4 db8 db16
25
N B M T N B M T N B M T N 72 09 17 98 73 08 17 98 72 09 17 98 B 0 37 01 38 0 38 0 38 0 38 0 38 M 04 03 19 26 04 03 19 26 04 03 19 26 T 76 49 37 162 77 49 36 162 76 50 36 162
50
N 74 06 18 98 74 06 18 98 74 06 18 98 B 0 38 0 38 0 38 0 38 0 38 0 38 M 02 02 22 26 02 02 22 26 02 02 22 26 T 76 46 40 162 76 46 40 162 76 46 40 162
75
N 69 06 23 98 69 06 23 98 69 06 23 98 B 0 36 02 38 0 36 02 38 0 36 02 38 M 02 01 23 26 02 01 23 26 02 01 23 26 T 71 43 48 162 71 43 48 162 71 43 48 162
100
N 68 03 27 98 68 03 27 98 68 03 27 98 B 0 36 02 38 0 36 02 38 0 36 02 38 M 02 01 23 26 02 01 23 26 02 01 23 26 T 70 40 52 162 70 40 52 162 70 40 52 162
N : Normal Images B:Benign Images M: Malignant Images T:Total
Table 6.12: Classification of mammogram images using Haar & Biorthogonal filters in DWT(Euclidean).
Coef. In % Wavelet filter :Haar Wavelet filter : Biorthogonal
25
N B M T N B M T N 72 09 17 98 68 03 27 98 B 0 37 01 38 0 36 02 38 M 04 03 19 26 02 01 23 26 T 76 49 37 162 70 40 52 162
50
N 74 06 18 98 68 03 27 98 B 0 38 0 38 0 36 02 38 M 02 02 22 26 02 01 23 26 T 76 46 40 162 70 40 52 162
75
N 69 06 23 98 68 03 27 98 B 0 36 02 38 0 36 02 38 M 02 01 23 26 02 01 23 26 T 71 43 48 162 70 40 52 162
100
N 68 03 27 98 68 03 27 98 B 0 36 02 38 0 36 02 38 M 02 01 23 26 02 01 23 26 T 70 40 52 162 70 40 52 162
N: Normal Images B: Benign Images M: Malignant Images T:Total
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 149
Table 6.13: Classification of mammogram images using Daubechies wavelet filters in DWT(Bray Curtis).
Coef. In %
Wavelet filter : Daubechies Db4 Db8 Db16
25
N B M T N B M T N B M T N 73 07 18 98 73 07 18 98 73 07 18 98 B 0 38 0 38 0 38 0 38 0 38 0 38 M 04 02 20 26 04 02 20 26 04 02 20 26 T 77 47 38 16
2 77 47 38 162 77 47 38 162
50
N 73 07 18 98 73 07 18 98 73 07 18 98 B 0 38 0 38 0 38 0 38 0 38 0 38 M 05 02 19 26 05 02 19 26 05 02 19 26 T 78 47 37 16
2 78 47 37 162 78 47 37 162
75
N 72 08 18 98 73 07 18 98 73 07 18 98 B 0 38 0 38 0 38 0 38 0 38 0 38 M 04 02 20 26 03 02 21 26 03 02 21 26 T 76 48 38 16
2 76 47 39 162 76 47 39 162
100
N 72 08 18 98 72 08 18 98 72 08 18 98 B 0 36 02 38 0 36 02 38 0 36 02 38 M 04 02 20 26 04 02 20 26 04 02 20 26 T 76 46 40 16 76 46 40 162 76 46 40 162
N: Normal Images B: Benign Images M: Malignant Images T:Total
Table 6.14: Classification of mammogram images using Haar & Biorthogonal wavelet filters in DWT(Bray Curtis).
Coef. In % Wavelet Filter : Haar Wavelet Filters : Biorthogonal
25
N B M T N B M T N 73 07 18 98 68 10 20 98 B 0 38 0 38 0 38 0 38 M 04 02 20 26 04 02 20 26 T 77 47 38 162 72 50 40 162
50
N 73 07 18 98 69 10 19 98 B 0 38 0 38 0 38 0 38 M 05 02 19 26 05 02 19 26 T 78 47 37 162 74 50 38 162
75
N 72 06 20 98 68 10 20 98 B 0 38 0 38 0 38 0 38 M 03 02 21 26 03 02 21 26 T 75 46 41 162 71 50 41 162
100
N 72 06 20 98 71 07 20 98 B 0 36 02 38 0 36 02 38 M 03 02 21 26 03 02 21 26 T 75 44 24 162 74 45 43 162
N: Normal image B: Benign image M: Malignant image T: Total
Chapter 6
150 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
From the Tables 6.11 to 6.14, we evaluated the classification accuracy
corresponding to different wavelet filters, different values of α and the
measures Euclidean and Bray Curtis. Results obtained are summarized in
Table 6.15 to 6.18. Further Sensitivity and Specificity are also evaluated for
each experiment and is given in Table 6.19 and 6.20.
Table 6.15: Successful classification rate (in %) of mammogram images using discrete Daubechies wavelet decomposition (Euclidean)
Coef. In %
Db4 Db8 Db16
N B M Performance N B M Perfor
mance N B M Performance
25
50
75
100
73.47
75.51
70.41
69.39
97.37
100.00
94.74
94.74
73.08
84.62
88.46
88.46
79.01
82.72
79.01
78.40
74.49
75.51
70.41
69.39
100.00
100.00
94.74
94.74
73.08
84.62
88.46
88.46
80.25
82.72
79.01
78.40
73.47
75.51
70.41
69.31
100
100
94.74
94.74
73.08
84.62
88.46
88.46
79.63
82.72
79.01
78.40
N: Normal B: Benign M: Malignant
Table 6.16: Successful classification rate (in %) of mammogram images using Haar & Biorthogonal discrete wavelet decomposition (Euclidean)
Coef. In %
Haar Biorthogonal N B M Performance N B M Performance
25
50
75
100
73.47
75.51
70.41
69.34
97.37
100.00
94.74
94.74
73.08
84.62
88.46
88.46
79.01
82.72
79.01
78.40
69.39
69.39
69.39
69.39
94.74
94.74
94.74
94.74
88.46
88.46
88.46
88.46
78.40
78.40
78.40
78.40
N : Normal B: Benign M:Malignant
Table 6.17: Successful classification rate (in %) of mammogram images using discrete Daubechies wavelet Decomposition (Bray Curtis)
Coef. In %
Db4 Db8 Db16 N B M Perfor
mance N B M Perfor
mance N B M Perfor
mance 25
50
75
100
74.49
74.49
73.47
73.47
100.00
100.00
100.00
94.74
76.92
73.08
76.92
76.92
80.86
80.25
80.25
79.01
74.49
74.49
74.49
73.47
100.00
100.00
100.00
94.47
76.92
73.08
80.77
76.92
80.86
80.25
81.48
79.01
74.49
74.49
74.49
73.47
100.00
100.00
100.00
94.74
76.92
73.01
80.77
76.92
80.86
80.25
81.48
79.01
N : Normal B: Benign M:Malignant
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 151
Table 6.18:-Successful classification rate (in %) of mammogram images using discrete Haar & Biorthogonal wavelet decomposition (Bray Curtis)
Coef. In %
Haar Biorthogonal N B M Performance N B M Performance
25
50
75
100
74.49
74.49
73.47
73.47
100.00
100.00
100.00
94.74
76.92
73.08
80.77
80.77
80.86
80.25
80.86
79.63
69.39
70.41
69.39
72.24
100.00
100.00
100.00
94.74
76.92
73.08
80.77
76.92
77.78
77.78
78.40
78.40
N : Normal B: Benign M:Malignant
Table 6.19: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in DWT (Euclidean)
Coef In %
Wavelet Filter: Db4
Wavelet Filter: Db8
Wavelet Filter: Db16
Wavelet Filter: Haar
Wavelet Filter: Bio
SN SP SN SP SN SP SN SP SN SP 25
50
75
100
73.08
84.62
88.46
88.46
86.76
86.76
81.62
78.68
73.08
84.62
88.46
88.46
87.50
86.76
81.62
78.68
73.08
84.62
88.46
88.46
87.50
86.76
81.62
78.68
73.08
84.62
88.46
88.46
86.76
86.76
81.62
78.68
88.46
88.46
88.46
88.46
78.68
78.68
78.68
78.68
SN = Sensitivity SP=Specificity
Table 6.20: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in DWT (Bray Curtis)
Coef In %
Wavelet Filter: Db4
Wavelet Filter: Db8
Wavelet Filter: Db16
Wavelet Filter: Haar
Wavelet Filter: Biorthogonal
SN SP SN SP SN SP SN SP SN SP 25
50
75
100
76.92
73.08
76.92
76.92
86.76
86.76
86.76
85.29
76.92
73.08
80.77
76.92
86.76
86.76
86.76
86.76
76.92
73.08
80.77
76.92
86.76
86.76
86.76
86.76
76.92
73.08
80.77
80.77
86.76
86.76
85.29
83.82
76.92
73.08
80.77
80.77
85.29
86.03
85.29
83.82
SN = Sensitivity SP=Specificity
An analysis of the performance of the different wavelet filters based
on Table 6.15 to 6.20 is given below.
i) Euclidean distance with α = 25%
Db8 wavelet filter gives the highest overall recognition rate –
80.25%.
Chapter 6
152 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Out of 98 normal mammogram images 73 (74.49 %) images are
correctly classified with db8 wavelet filter.
100% classification accuracy is obtained for benign type with db8 as
well as db16wavelet filter.
Out of 26 malignant images, 23 (88.46 %) images could be correctly
identified with Biorthogonal wavelet filter and is much better than the
values obtained for all other wavelet filters.
Using α = 25% wavelet coefficients, we obtained 88.46% sensitivity
in Biorthogonal wavelet filter followed by 73.08% sensitivity for all
other wavelet filter.
Using α = 25% wavelet coefficients, the highest specificity obtained is
87.50% for db8 and db16 wavelet filter.
ii) Euclidean distance with α = 50%
Except Biorthogonal (78.40%), all other wavelet filters gave 82.72%
recognition rate.
Out of 98 normal images 74 (75.51%) images are correctly classified
with all wavelet filters except Biorthogonal.
All the 38 (100%) benign images in the dataset are classified exactly
with all wavelet filters except Biorthogonal.
As in the case of α = 50%, the classification accuracy obtained for
malignant type with Biorthogonal is 88.46% whereas other wavelet
filters gave only 84.62%.
Using α = 50%, obtained highest sensitivity (88.46%) using
Biorthogonal wavelet filters.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 153
The highest Specificity obtained is 86.76% for all wavelet filters
except Biorthogonal filter.
It is observed that the performance does not improve with higher
values of α (75% and 100%). With Euclidean distance and DWT, α = 50% is
found to be ideal. Inclusion of more coefficients to the feature vector found to
have a negative impact on the classification accuracy. Of the wavelet filter
db8 is the best choice if overall classification accuracy is the criteria whereas
Biorthogonal is found to be the best choice for detecting malignant cases. The
Figures 6.9 to 6.12 show the variation of classification performance with
different α for DWT and Euclidean distance measure.
Figure 6.9: Classification performance of mammogram images using 25% DWT wavelet
transofrmation coefficients ( Euclidean distance)
Figure 6.10: Classification performance of mammogram images using 50% DWT wavelet
transofrmation coefficients ( Euclidean distance)
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce
in%
Normal
Benign
Malignant
Overall
Chapter 6
154 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.11: Classification performance of mammogram images using 75% DWT wavelet
transofrmation coefficients ( Euclidean distance)
Figure 6.12: Classification performance of mammogram images using 100% DWT wavelet
transofrmation coefficients ( Euclidean distance)
iii) Bray Curtis distance measure
The best results obtained are summaried below:
The highest classification accuracy 81.48% is obtained with α = 75%
and wavelet filter db8 and db16.
Out of 98 normal mammogram images 73 (74.49%) images are
classified correctly with the different filters except Biorthogonal for α
= 25%, 50% and 75%.
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 155
Almost all benign images are classified correctly with all wavelet
filters for α = 25%, 50% and 75%.
Out of 26 malignant images 21 (80.77 %) are correctly classified
with wavelet filters except db4 (α = 75%).
The optimum sensitivity obtained is 80.77% for all wavelet filters
except db4 filter using α = 75%.
The highest specificity obtained is 86.76% for all Daubechies filters
withα = 25%, 50% and &75%).
By comparing the overall performance of the classification with
different α and Bray Curtis measure, α = 75% make excellent results
compared to other α values. The performance of the classification of
mammogram images using different wavelet filters, Bray Curtis distance
measure and different α values are shown in Figure 6.13 to 6.16.
Figure 6.13: Classification performance of mammogram images using 25% DWT wavelet
transofrmation coefficients (Bray Curtis)
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
Chapter 6
156 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.14: Classification performance of mammogram images using 50% DWT wavelet
transofrmation coefficients (Bray Curtis)
Figure 6.15: Classification performance of mammogram images using 75% DWT wavelet
transofrmation coefficients (Bray Curtis)
Figure 6.16: Classification performance of mammogram images using 100% DWT wavelet
transofrmation coefficients (Bray Curtis)
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
0102030405060708090
100
db4 db8 db16 Haar Biorthogonal
Clas
sific
atio
n pe
rfor
man
ce in
%
Normal
Benign
Malignant
Overall
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 157
Finally the overall performance of the classification algorithms
employing DWT and the two distance measures are summarized in Table 6.21.
Table 6.21: Overall performance of the classification algorithm employing DWT with different α values.
Distance measure
Wavelet Coefficients (α) In %
Normal Benign Malignant Performance
Euclidean 50 75.51 100 88.46 82.72
Bray Curtis 75 74.49 100 80.77 81.48
From Table 6.21 it is evident that Euclidean distance measure is better
than Bray Curtis in the classification of mammogram based on DWT
features. The Figure 6.17 shows the overall performance of the classification
algorithms in DWT with the two different distance measures.
Figure 6.17: Overall performance of the classification accuracy in DWT based on two different
distance mesures.
6.3.2.3 Comparative Performance Evaluation of SWT and DWT
A comparative analysis of the performance of SWT and DWT is
carried out on the basis of the classification accuracy obtained. The results
clearly show that the classification of mammogram images using DWT is far
better than SWT for abnormal images in the dataset. This is because the
0102030405060708090
100
Normal Benign Malignant Overall
Clas
sific
atio
n pe
rfor
man
ce in
%
Euclidean
Bray Curtis
Chapter 6
158 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
DWT coefficients have the ability to characterize the varying nature of pixel
intensities around the abnormal area of the image. Even though SWT
coefficients are redundant in nature, they under perform in characterizing the
variation of pixel intensity in abnormal area of ROI. The outcome of the
experiments is projected in the following Figures 6.18 to 6.27.
Figure 6.18: Classification performance of Daubechies db4 Wavelet (Euclidean distance)
Figure 6.19: Classification performance of Daubechies db4 Wavelet (Bray Curtis distance)
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 159
Figure 6.20: Classification performance of Daubechies db8 Wavelet (Euclidean distance)
Figure 6.21: Classification performance of Daubechies db8 Wavelet (Bray Curtis distance)
Figure 6.22: Classification performance of Daubechies db16 Wavelet (Euclidean distance)
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sifica
tion
perf
orm
ance
in%
25
50
75
100
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Statioanry Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
Chapter 6
160 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.23: Classification performance of Daubechies db16 Wavelet (Bray Curtis distance)
Figure 6.24: Classification performance of Haar wavelet (Euclidean Distance)
Figure 6.25: Classification performance of Haar Wavelet(Bray Curtis)
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
0102030405060708090
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 161
Figure 6.26:Classification performance of Biorthogonal Wavelet (Euclidean Distance)
Figure 6.27 : Classification performance of Biorthogonal Wavelet(Bray Curtis)
6.3.2.4. Summary of Experiments with Wavelet Features and Distance
Measures
Based on the experiments carried out with SWT and DWT (6.3.2.1 to
6.3.2.3), we arrived at the following:
The accuracy obtained in Euclidean distance measure is better than
the Bray Curtis distance measure in all cases.
Discrete Wavelet Transformation results in more distinguishing
feature vector than Stationary Wavelet Transformation and hence
outperforms DWT in classifications.
0
20
40
60
80
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
0
20
40
60
80
100
Normal Benign Malignant Performance Normal Benign Malignant Performance
Stationary Wavelet Transformation Discrete Wavelet Transformation
Clas
sific
atio
n pe
rfor
man
ce in
%
25
50
75
100
Chapter 6
162 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Stationary Wavelet Transformation gives slightly better results in
classifying normal images.
Classification accuracy obtained in the case of benign type images are
100 percent for all the Wavelet filters used in the classification except
Biorthogonal wavelet.
Classification accuracy obtained for malignant type images are also
high and same for most of the wavelet filters used in the
classification.
The percentage of wavelet transformation coefficients, α, influences
the recognition accuracy.
6.3.3 Classification of Mammogram Images using GLCM Features
The four different GLCM features such as Contrast, Energy,
Homogeneity and Correlations in four different orientations are extracted as
explained in section 3.4.5 of chapter 3. These features are combined to form a
unique feature vector for the classification of mammogram images. The
classification is done using two different distance measures viz Euclidean and
Bray Curtis. We extracted a set of 322 ROIs from the original mammogram
images from the Mini-MIAS database. This database mainly contains three
types of images normal, benign and malignant. ROIs of size 124x124 pixels
around the origin of abnormality are extracted for both benign and malignant
classes. In the case of normal images, ROIs are extracted around the center of
the mammogram images. 10% of the ROIs are randomly selected for
extracting the GLCM features and kept as training set. The GLCM features
extracted in different orientations are combined to form a feature vector and
subsequently they are used for constructing the class core vector for the
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 163
training purpose. The class core vector of the GLCM feature is constructed
using equation (6.6).
∑=
=N
ii
im AC
1N1 (6.6)
where Cm is the mth class core vector. N is the number of ROIs selected to
produce the class core vector, Ai is the set of 16 different features generated
from GLCM and m is the number of classes of images in the dataset.
In the testing part, each ROIs belonging to the test group is classified
using the distance between the feature vector and class core vector. The
system automatically classifies the test image by finding the minimum
Euclidean distance between the feature vector of the test image and the class
core vector of each class using equation (6.7).
∑=
−=16
1i
2m )]()([ )C ,( iCiAADist m (6.7)
where A is the feature vector of the test image, and Cm is the class core vector
for each class m. For comparative analysis we also employed Bray Curtis
distance measure defined in equation (6.8).
∑
∑
=
=
+
−= 16
1
16
1im
|)(|
|)(| )C(A,
i
im
im
CiA
CiADist (6.8)
6.3.3.1 Results and Discussion
We implemented the GLCM feature based classification algorithms
and applied on the data samples taken from the Mini-MIAS dataset consisting
of Normal, Benign and Malignant images. Class core vectors are created
Chapter 6
164 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
using 10 % of images from each class, selected at random. We have chosen
162 mammogram ROIs from the dataset which contains 98 normal, 38
benign and 26 malignant images for testing purpose. The classification results
obtained using GLCM feature and the two different distance measures are
shown in Table 6.22 and corresponding percentage (%) of accuracy of the
classification is given in Table 6.23.
Table 6.22: Classification of mammogram images using GLCM feature in Euclidean and Bray Curtis distance measures
Euclidean Distance Measure Bray Curtis Distance measure M B N Total M B N Total
M 24 01 01 26 23 01 02 26
B 05 33 0 38 04 30 04 38
N 14 07 77 98 20 07 71 98
N: Normal B: Benign M: Malignant
The Table 6.22 is the confusion matrix generated based on the
classification algorithm. Out of 98 normal images, the GLCM based classification
algorithm correctly classified and labeled 77 images with Euclidean distance and
71 images with Bray Curtis distance measure. In respect of 38 benign images, 33
images are correctly classified and labeled by Euclidean distance and 30 images
by Bray Curtis distance. Finally out of 26 malignant images, 24 images are
correctly classified using the Euclidean distance where as 23 images are classified
correctly in Bray Curtis distance. The overall performance of the classifications
using GLCM features is plotted in Figure 6.28.
Table 6.23: Successful classification rate (in %) of mammogram images using GLCM features in Euclidean and Bray Curtis distance measures.
Distance Measure
Normal in %
Benign in %
Malignant in %
Overall Performance(%)
Sensitivity in % (SN)
Specificity in % (SP)
Euclidean 78.57 86.84 92.31 82.72 92.31 86.13
Bray Curtis 72.45 78.95 88.46 76.54 88.46 82.48
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 165
Figure 6.28 : Classification performance of mammogram images using GLCM feature using
Euclidean and Bray Curtis distance measure.
From the results obtained it follows that Euclidean distance measure is
more suitable for classification of mammogram images employing GLCM
features.
6.3.4 Comparative Performance Evaluation of DWT and GLCM
Features
In general comparing the performance of the classification of
mammogram images using DWT and GLCM features, it reveals that the
classification accuracy obtained by DWT is better than GLCM features. In
Euclidean distance measure the overall performance of the classification
using the two features are same (82.72%) whereas in Bray Curtis distance
measures it is 81.48% and 76.54% for DWT and GLCM features
respectively. The individual classification performance of GLCM for normal
and malignant images is far better than DWT features. At the same time the
classification accuracy obtained for benign images are 100% using DWT
features. The comparative performance of the classification using DWT and
GLCM is shown in below Figure 6.29.
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Normal Benign Malignant overall
Clas
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Euclidean
Bray Curtis
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166 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.29: Comaprative classification performance of DWT and GLCM features in Euclidean
and Bray Curtis distance measures
6.4 Classification of Mammogram Images using GLCM Features
and Lazy Classifiers
Lazy learning classifiers are instance based or memory based
classification algorithm proposed against the common eager learning algorithms.
They are the important category of classifiers that can be implemented and tested
easily with minimum cost. These learning algorithms make use of a kind of
distance measure between test instances and training instances for the
classification. Entropy and distance measures are the two common methods
adopted by most of the lazy classifiers. In this section we propose a novel
approach based on lazy classifiers for mammogram classification.
6.4.1 Classification of Mammogram Images
Unlike the previous sections, here we present a complete classification
system for the detection and classification of abnormalities in digital
mammograms. A novel approach incorporating GLCM features and lazy
classifiers is proposed. In the new approach a two stage classification system is
visualized. In the first stage a method is devised for identifying and classifying
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Normal Benign Malignant Performance Normal Benign Malignant Performance
DWT GLCM
Clas
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Euclidean
Bray Curtis
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 167
the risk level of the mammograms. i.e. normal and abnormal. In the second
stage, all the abnormal images are first categorized into benign and malignant
followed by further sub classification of benign and malignant images into
different sub categories based on the types of abnormalities or distortions
present in the mammograms such as calcification, asymmetric distortion,
architectural distortion, circumference masses, speculated and ill defined
masses. The architecture of the proposed system is shown in Figure 6.30.
Figure 6.30: Architecture of the classification of mammogram using GLCM and Lazy classifiers
Mammogram Acquisition
Extraction of ROIs
Construction of GLCMs of ROIs
Compute GLCM Feature Vector
First Level Classification
Normal Abnormal
Second Level Classification
ARCH CALC SPIC ASYM CIRC
MISC
Benign
Malignant
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168 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
For classification, GLCM features are extracted from the ROIs of the
dataset. The GLCM matrices are generated in four different orientations for
four different sizes of ROIs (8 x 8, 16 x 16, 32 x 32 and 64 x 64 pixel sizes).
The GLCMs are constructed by taking pair of image cells at d =1 distance
apart and incrementing the matrix position corresponding to the gray level of
both cells. Thus the system generated four different GLCMs in four different
orientations such as 00,450,900and 1350 as explained in section 3.4.5. From
the GLCMs, Contrast (f1x), Energy (f2x), Homogeneity (f3x) and
Correlation (f4x) of the gray level values are extracted. The four features
extracted from the different orientations of the GLCM matrix are combined
together to form a feature vector, which comprises a set of 16 values. This
feature vector is used for the classification.
The classifier is trained using the feature vector extracted by the different
sets of ROIs of size 8×8, 16×16, 32×32 and 64×64 pixels from the images in
Mini-MIAS database. The most common lazy learning algorithms such as K*,
IBL and LWL are used for training and testing. The training and testing datasets
of the ROIs are prepared by dividing the entire dataset into ten different folds of
equal sizes. Then nine different folds of dataset are used for the training and the
remaining one folder of the dataset is used for testing. The processes of training
and testing are repeated for each set of folders and the performance is evaluated
by taking the average of test result obtained in each case.
6.4.2 Algorithm for Mammogram Image Classification Using Lazy
Classifiers
Step 1 : Extract mammogram ROIs of different sizes (64 × 64, 32 × 32
pixels, 16 × 16 pixels and 8 × 8 pixels) based on the abnormality
center of the original mammogram images.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 169
Step 2 : From the extracted ROIs, the Gray level co-occurrence matrices in
four different orientations such as 00, 450, 900 and 1350 are
constructed for each size of the ROIs.
Step 3 : The GLCM features contrast(C), Energy (E), Homogeneity (H) and
correlations(R) are computed for each GLCM constructed in step 2.
Step 4 : Form a feature vector of 16 values which comprising the features
computed at step 3 in all four different GLCMs constructed from
the ROIs.
Step 5 : The feature vector computed in step 4 is grouped as training and
testing sets for classification.
Step 6 : For each training and testing set pair,
- Train the classifiers with the training set.
- Evaluate the classification performance with test set.
Step 7 : Obtain average performance for each classifier employed.
6.4.3 Results and Discussion
The dataset used for the experiment comprised of 330 ROIs extracted
from 322 mammogram images from the Mini-MIAS database. The set
consists of 207 normal, 54 malignant and 69 benign images. The different
window sizes of ROIs (8 × 8, 16 × 16, 32 × 32 and 64 × 64 size of pixels) of
each mammogram image in the dataset are extracted based on the
abnormality center of the image. For each window size, the 16 GLCM feature
values are computed, forming the feature vector. By using this feature vector,
the ROIs are classified with the three different lazy classifiers K*, IBL and
LWL. The classification is done in two different levels. In the first level, the
instances are classified based on the risk level into Normal and Abnormal. In
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170 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
the second level, all the abnormal images are separated into benign and
malignant type and identified to which sub categories of benign and
malignant the samples belongs to. The performance of the classification
algorithm is evaluated based on the accuracy, sensitivity and specificity. The
confusion matrix generated by the three classifiers in the first level of the
classification is shown in Table 6.24.
Table 6.24: Confusion matrix generated by different Lazy classifiers on Mini-MIAS database at the first stage of the classification
ROIs Size in Pixels
K* IBL LWL
8x8
Abnormal Normal Abnormal Normal Abnormal Normal
Abnormal 39 84 36 87 09 114
Normal 04 203 01 206 0 207
16x16 Abnormal 70 53 68 55 10 113
Normal 01 206 0 207 0 207
32x32 Abnormal 99 23 96 26 12 110
Normal 01 206 0 207 02 205
64x64 Abnormal 111 11 112 10 08 114
Normal 0 207 0 207 0 207
From the above confusion matrix, the accuracy, sensitivity (SN) and
specificity (SP) of the three lazy classifiers are evaluated on different sizes of
ROIs. The evaluation results are shown in Table 6.25.
Table 6.25: Classification accuracy of mammogram images using Lazy classifiers at level 1
ROI Size
K* IBL LWL Accuracy SN SP Accuracy SN SP Accuracy SN SP
8 x 8
16 x 16
32x32
64x64
73.33
83.64
92.70
96.65
31.71
56.91
81.15
90.98
98.07
99.52
99.52
100
73.33
83.33
92.10
96.96
29.27
55.28
78.69
91.80
99.52
100
100
100
65.45
65.76
65.96
65.34
7.31
8.13
9.84
6.55
100
100
99.03
100
SN = Sensitivity SP = Specificity
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 171
From the above table, the following conclusions are drawn
The highest accuracy of the classification is obtained for ROIs of size
64 × 64.
Using the IBL classifier, we achieved highest classification accuracy
96.96 % with ROIs of size 64 × 64 window which is followed by K*
with 96.65%
On increasing ROI window size from 8 × 8 to 64 × 64, both K* and
IBL classifier shows the gradual improvement on the performance
accuracy.
It is found that the performance of the accuracy obtained in LWL
classifier is poor compared to other two classifiers.
As far as LWL classifier is concerned, the accuracy of the
classification algorithm remains almost same for all ROIs size.
The performance of the classification algorithm in terms of accuracy
is shown in Figure 6.31.
Figure 6.31: The classification accuracy(in %) of the various Lazy Classifiers using different
sizes of ROIs
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8 x 8 16 x 16 32 x 32 64 x 64
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Kstar
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LWL
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The Sensitivity (SN) and Specificity (SP) are the other two
parameters that can be used for quantifying the performance of the
classification. The sensitivity of K* algorithm is continuously increases as
the ROIs window size increases from 8 × 8 pixels to 64 × 64 and it achieved
90.98% for window size of 64 × 64 pixels. The specificity also increases
from ROIs window size 8 × 8 pixels to 64 × 64 pixels and achieved 100% for
ROIs size 64 × 64 pixels. In the case of IBL, the sensitivity is 100 % for all
ROIs size except 8 × 8 window size. For IBL also, the specificity is increases
with ROIs window size. Eventhough the LWL classifier’s accuracy is less
compared to other two classifier, the sensitivity is 100 % for all ROIs sizes
execept for 32 × 32 pixels. The sensitivity is almost constant for this
classifier compared to K* and IBL, all these measures are shown
diagramatically in Figure 6.32.
Figure 6.32: The performance of various classifiers in % with respect to Sensitivity and Specificity
In the second level of the classification, all the abnormal images
labeled in the database are further classified into either benign or malignant
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Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity
K* IBL LWL
Clas
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%
8x8
16x16
32x32
64x64
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 173
categories using the above three lazy classifiers. At the same time all the
benign and malignant images are further classified into different
subcategories such as Calcification, Architectural distortion, Asymmetric
distortion, Circular distortion, Ill defined and Speculation. The confusion
matrix generated by the three lazy classifiers on the second level of
classification is shown in Table 6.26.
Table 6.26: Confusion matrix generated by different Lazy classifiers on Mini-MIAS Database in second stage of the classification.
ROIs Size in Pixels
K* IBL LWL
8x8
Malignant Benign Malignant Benign Malignant Benign
Malignant 24 30 24 30 18 36
Benign 0 69 0 69 02 67
16x16 Malignant 38 16 37 17 21 33
Benign 01 68 0 69 04 65
32x32 Malignant 42 11 42 11 31 22
Benign 0 69 0 69 18 51
64x64 Malignant 53 0 48 05 33 20
Benign 04 65 0 69 25 44
From the confusion matrix, the classification performance evaluation
parameters such as accuracy, sensitivity and specificity of the three lazy
classifiers are computed and shown in Table 6.27.
Table 6.27: Classification accuracy (in %) of mammogram images using different Lazy classifiers at level 2
ROI Size K* IBL LWL Accuracy SN SP Accuracy SN SP Accuracy SN SP
8 x 8
16 x 16
32x32
64x64
75.60
86.17
90.98
96.72
44.44
70.37
79.25
100
100
98.55
100
94.20
75.60
86.17
90.98
95.90
44.44
68.51
79.25
90.57
100
100
100
100
69.10
69.91
67.21
63.11
33.33
38.88
58.49
62.26
97.10
94.20
73.91
63.77
SN = Sensitivity SP = Specificity
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174 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
From the above table we derived the following conclusions:
The overall accuracy of the system increases with increase in ROI
size, both K* and IBL classifiers.
K* classifier gave the highest accuracy (96.72%) followed by IBL
(95.90%), both with 64x64 window size.
Compared to K* and IBL, the classification accuracy obtained in
LWL classifier is poor.
Using K* classifier we obtained 100% sensitivity for ROIs window
size 64 × 64 pixels. But IBL classifier also gave the highest sensitivity
of 90.57 % for the same ROIs window size.
The highest sensitivity for LWL classifier is 62.26% with ROIs
window size 64 × 64 pixels. It is noted that, for LWL also the
sensitivity is gradually increasing as we increase the ROIs window
size.
The highest specificity obtained is 100% in K* with ROIs window
size 8x8 pixels and 32 × 32 pixels respectively which is followed by
98.55% for ROIs with 16x16 pixel size.
Using LWL classifier, we obtained only 69.91% accuracy on ROI
window size 16 × 16 pixels. As far as specificity is concerned, it is
gradually decreasing as on increasing the ROI window size in contrast
to sensitivity.
The accuracy as well as sensitivity and specificity of the classification
of mammogram images into malignant and benign type by the three lazy
classifiers are shown diagrammatically in Figures 6.33 and 6.34 respectively.
Classification Algorithms for Detection of Abnormalities in
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Figure 6.33: The overall performance(in %) evaluation of the various Lazy Classifiers using different sizes of ROIs
Figure 6.34: The performance of various classifiers in % with respect to Sensitivity and Specificity
Finally the sub category wise classification based on the type of
abnormalities of all the abnormal images in the dataset are done with the
above three lazy classifier
classifiers on these classification is shown in Table 6.
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Sensitivity Specificity
K*
Clas
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assif
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%
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
The overall performance(in %) evaluation of the various Lazy Classifiers using sizes of ROIs
The performance of various classifiers in % with respect to Sensitivity and Specificity
Finally the sub category wise classification based on the type of
abnormalities of all the abnormal images in the dataset are done with the
above three lazy classifiers. The confusion matrices generated by the
classifiers on these classification is shown in Table 6.28.
Specificity Sensitivity Specificity Sensitivity Specificity
IBL LWL
Mammogram Images
175
The overall performance(in %) evaluation of the various Lazy Classifiers using
The performance of various classifiers in % with respect to Sensitivity and Specificity
Finally the sub category wise classification based on the type of
abnormalities of all the abnormal images in the dataset are done with the
s. The confusion matrices generated by the
Specificity
8x8
16x16
32x32
64x64
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Table 6.28: Confusion matrix generated by different lazy classifiers on Mini-MIAS Database into different categories of the mammogram images.
1: CALC 2: CIRC 3: ARCH 4: ASYM 5: MISC 6: SPIC
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 177
From this confusion matrix, the accuracy of the classification achieved
by the above three classifiers are evaluated. The accuracy obtained by the
three classifiers is shown in Table 6.29.
Table 6.29: Classification accuracy of mammogram images using different Lazy Classifiers into different categories of the mammogram images
ROI Size K* IB1 LWL 8 x 8
16 x 16
32x32
64x64
50.41
67.48
86.18
95.08
50.41
65.04
86.18
95.08
33.33
38.21
37.40
35.25
The table reveals that, the performance of the lazy classifier K* and
IBL increase from ROIs size varying from 8 × 8 pixels to 64 × 64 pixels. In
both classifiers, the highest accuracy rate obtained is 95.08 % for 64 × 64 size
ROIs. The performance of LWL classifier is even though very poor, but it has
sudden decrease in their performance from ROIs of size 16 × 16 pixel
onwards as we seen in the first and second level classification. The
performance of the above three lazy classifiers in sub category wise
classification is shown in Figure 6.35.
Figure 6.35: The performance(in %) evaluation of the various Lazy Classifiers using different
sizes of ROIs
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8 x 8 16 x 16 32 x 32 64 x 64
Clas
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Kstar
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LWL
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178 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
6.5 Classification of Mammogram Images Using Wavelet
Features and Lazy Classifiers.
In this section we propose a new method for classifying mammogram
images using lazy classifiers such as Kstar, IBL and LWL and wavelet
transformation coefficients. The wavelet transformation coefficients obtained
after the decomposition of the images in multi levels have high
dimensionality. Processing of these high dimensional data or coefficients as
feature is very time consuming and often results in performance degradation.
So dimensionality reduction method is required for reducing the size of
feature vectors. The Principal component Analysis (PCA) is a common and
simple technique used for reducing the dimensionality of feature sets.
6.5.1 Principal Component Analysis
Principal Component Analysis (PCA) is a mathematical algorithm that
reduces the dimensionality of the data while retaining most of the variation in
the dataset. It accomplishes the reduction by identifying directions called
principal components along which the variation in the data is maximal. By
using PCA, each sample can be represented by relatively few numbers
instead of by values for thousands of variables. Samples can then be plotted,
making it possible to visually assess similarities and differences between
samples and determine whether samples can be grouped or not [Jolliffe,
2002].
PCA identifies new variables, the principal components, which are
linear combinations of the original variables. It is easy to see that the first
principal component is the direction along which the samples show the
largest variation. The second principal component is the direction
uncorrelated to the first component along which samples show the largest
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 179
variation. If dataset are standardized such that each element in the dataset is
centered to zero average matrix of the element in the dataset and ordered
according to how much of the variation present in the dataset contain. Each
component can then be interpreted as the direction, uncorrelated to previous
components, which maximizes the variance of the samples when projected
onto the component [Ringner, 2008].
6.5.2 Classification of Mammogram Images Using PCA Components
of Wavelet Features and Lazy Classifiers
In this section, we proposes a multi-level classification of mammogram
images for analyzing texture characteristics using the reduced wavelet
transformations coefficients and lazy learning classifiers. The classification is
accomplished by extracting the ROIs of size 64x64 pixels. The ROIs of all the
abnormal images are extracted based on the abnormality centre of the image
whereas the ROIs of normal images are extracted based on the centre of the
abnormality. After extracting the ROIs, they are decomposed into three different
levels using discrete wavelet transformations. The approximation coefficient
which characterizes the image is represented by high dimensional data and is
redundant in nature. Feature vectors are constructed by reducing the redundant
data in approximation coefficients using PCA. Using this feature vector,
classification of the ROIs is performed. Classification is accomplished in two
different levels, in the first level all the ROIs extracted from the dataset are
classified into normal and abnormal. In the next level of the classification, all the
abnormal images identified in the first level of the classification are identified
and labelled into benign and malignant types. The sub level classification of
abnormal images in the dataset such as calcification (CALC), asymmetry
(ASYM), Architectural distortion (ARCH), Circumscribed masses (CIRC), ill
defined masses (MISC) and speculated masses (SPIC) are also carried out. The
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180 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
lazy learning classifiers K*, IBL and LWL are used for classificatio
diagram of the proposed system is shown in
Fig.6.36: Block diagram
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
lazy learning classifiers K*, IBL and LWL are used for classification. The
of the proposed system is shown in Figure 6.36.
of the mammogram image classification using PCA and Lazy classifiers
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
n. The block
of the mammogram image classification using PCA and Lazy classifiers
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 181
6.5.3 Results and Discussion
We implemented the above classification algorithms using MATLAB
and WEKA software. The wavelet features are extracted using Biorthogonal
wavelet filter on the ROIs of size 64 × 64 pixels. The ROIs are decomposed
into three different levels in DWT. All the approximation coefficients
obtained in the decomposition level three are further reduced using the PCA.
The highest 16 Eigen values obtained for representing the major principal
components of the approximation coefficients are used as the feature vector.
Finally the classification is performed using three different lazy classifiers
such as K*, IBL and LWL available in WEKA.
The performance of the classification algorithms could be assessed using
the different performance parameters discussed in chapter 2. The confusion
matrix generated by the K*, IBL and LWL classifiers during the classification of
normal and abnormal images are shown in Table 6.30. The three performance
evaluation parameters viz. Sensitivity, Specificity and Accuracy computed based
on the confusion matrix (Table 6.30) are given in Table 6.31.
Table 6.30: Confusion matrix generated for classifying Mammogram images into Normal and Abnormal using different Lazy classifiers.
K* IBL LWL Abnormal Normal Abnormal Normal Abnormal Normal
Abnormal 122 0 122 0 8 114
Normal 0 207 0 207 0 207
Table 6.31: Classification accuracy in Normal and Abnormal classification of mammograms using different Lazy classifiers.
Classifiers Sensitivity Specificity Accuracy K* 100 100 100
IB1 100 100 100
LWL 6.55 100 65.35
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182 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
From the above two tables, following conclusions are drawn:
Using K* and IBL classifier, all the normal and abnormal images in
the dataset are exactly identified and labeled.
Even though LWL classifier classified all the normal images, at the
same time most of the abnormal images are also classified wrongly as
normal.
Both K* and IBL classifier achieved 100% sensitivity and specificity
for classifying 329 mammogram ROIs from the dataset.
Using LWL classifier we obtained only 64% sensitivity but 100%
specificity is obtained for classifying 329 ROIs from the dataset.
The accuracy obtained by K* and IBL classifiers are 100% and
65.35% for LWL classifier.
The overall performance of the above classification is graphically
shown in Figure 6.37.
Figure 6.37: Classification performance of mammogram images into normal and abnormal
category using PCA and Lazy classifiers.
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Sensitivity Specificity Accuracy
Clas
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%
K*
IB1
LWL
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 183
In the second level of the classification, all the abnormal images in the
dataset are classified into benign and malignant types. The confusion matrix
shown in Table 6.32 indicates that out of 122 abnormal images, all the 69
benign and 53 malignant images are correctly identified and classified by K*
and IBL. But in LWL classifier, out of 69 benign images 41 benign images
and out of 53 malignant images 42 malignant images are also correctly
identified and classified. The sensitivity, specificity as well as accuracy
obtained by the K* and IB1 are 100%. But for LWL classifier, they are
78.89%, 60% and 68% respectively. The confusion matrix and the
classification performance are given in Table 6.32 and Table 6.33. The
corresponding graphical representation is shown in Figure 6.38.
Table 6.32: Confusion matrix generated for classifying Mammogram images into benign and malignant using different Lazy classifiers
K* IBL LWL Malignant Benign Malignant Benign Malignant Benign
Malignant 53 0 53 0 42 11
Benign 0 69 0 69 28 41
Table 6.33: Classification accuracy in benign and malignant classification of mammograms
using different Lazy classifiers.
Classifier Sensitivity Specificity Accuracy K* 100 100 100
IB1 100 100 100
LWL 79.25 59.42 68
From the above tables, we can draw following conclusions:
Both K* and IBL classifiers are good lazy classifiers classified all the
abnormal mammogram images into benign and malignant types and
achieved 100% classification accuracy, sensitivity and specificity.
Compared to K* and IBL, the performance of the LWL classifier is poor.
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184 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Even though the Sensitivity and Specificity obtained by LWL
classifier is less, it has significant improvement on reduced Wavelet
approximation coefficients.
The overall performances of the above three classifiers are shown in
Figure 6.38.
Figure 6.38: Classification performance of mammogram images into benign and malignant
category using PCA and Lazy classifiers.
Finally, all the abnormal mammogram images in the dataset are
further classified into respective sub categories depending on the texture
feature distribution of the ROIs in the images. Table 6.34 shows the
confusion matrix obtained by the different lazy classifiers. This table reveals
that both the K* and IBL classifier exactly classified all the abnormal images
into six different subcategories. But the LWL classifier classified all the
abnormal images in a different way than K* and IBL. From the Table 6.34,
we can make a conclusion that the LWL classifier classifies most of the
abnormal images into circumscribed masses instead of the respective
subcategory. The accuracy obtained by the multilevel classification is 100%
for K* and IB1 classifier whereas, it is only 31.71% for LWL classifier. The
0
10
20
30
40
50
60
70
80
90
100
K* IBL LWL
Clas
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Sensitivity
Specificity
Accuracy
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Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 185
overall accuracy of the multi-level classification is shown in Table 6.35 and
its graphical representation is shown in Figure 6.39.
Table 6.34: Confusion matrix generated for classifying abnormal mammogram images into different sub categories of abnormalities using different Lazy classifiers
K* IBL LWL C A Y R M S C A Y R M S C A Y R M S
CALC (C) 30 0 0 0 0 0 30 0 0 0 0 0 8 0 0 22 0 0
ARCH (A) 0 19 0 0 0 0 0 19 0 0 0 0 1 5 0 13 0 0
ASYM(Y) 0 0 15 0 0 0 0 0 15 0 0 0 4 0 0 11 0 0
CIRC (R) 0 0 0 25 0 0 0 0 0 25 0 0 0 0 0 25 0 0
MISC (M) 0 0 0 0 15 0 0 0 0 0 15 0 0 0 0 15 0 0
SPIC (S) 0 0 0 0 0 19 0 0 0 0 0 19 3 0 0 15 0 1
Table 6.35: Classification accuracy in sub categories of mammograms
Classifier Accuracy (%) K* 100
IB1 100
LWL 31.71
Figure 6.39 : Classification accuracy obtained for Classification of mammogram using PCA and
Lazy classifiers
100 100
31.71
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K* IB1 LWL
Clas
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Accuracy (%)
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186 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
6.6 Detection and Classification of Mammogram Images Using
Artificial Neural Network and Extreme Learning Machine
Artificial Neural Network (ANN) is the most popular machine
learning algorithm used in many pattern recognition applications. Even
though the learning mechanism adopted by ANN is very slow, they
outperform than other approaches in most of the classification task. Learning
process in ANN is relatively slow because all the parameters used in the
neural networks are tuned iteratively while using gradient-based learning
algorithms for training. One of the important constraints that reduce the
performance of ANN is its architecture. The architecture of ANN becomes
more complex with the introduction of more hidden layers in the network.
Recently a new learning paradigm called Extreme Learning Machine (ELM)
is introduced as an alternative to the existing machine learning algorithm
which has only single hidden layer with a linear learning strategy.
6.6.1 Classification of Mammogram Images using ANN and ELM
In this section we present a new machine learning algorithm in
conjunction with ANN for classifying the abnormal mammogram images in
the Mini-MIAS database as benign and malignant types. Initially all the
abnormal images in the dataset are automatically segmented for extracting
the ROIs using the segmentation algorithm discussed in chapter 5. After
segmenting ROIs, the two different features sets GLCM and Wavelet
Transformation coefficients, as explained in chapter 3, are extracted from the
ROIs. The classification experiments are carried out with Multi Layer
Perceptron (MLP) as well as Extreme Learning Machine (ELM). Further, in
the case of wavelet features, the high dimensional feature vectors constructed
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 187
from the ROIs are reduced using Principal Component Analysis (PCA) for
providing optimum number of inputs for the MLP and ELM.
In ANN classification, we constructed a multi-layer perceptron
(MLP), which is a typical neural network with parallel distributed
information processing structure consisting of processing elements
interconnected by directional connections [Thangavel et.al, 2005b]. The
network is defined with n (number of features in the feature vector) input
nodes, n hidden nodes and two output nodes. The sigmoid activation function
is used for training the network.
The research on approximation capabilities of feed forward neural
networks has focused on two aspects: universal approximation on compact
input sets and approximation in a finite set of training samples. In real
applications, neural networks are trained in finite training set. For function
approximation in a finite training set, [Huang and Babri, 1998] showed that a
single hidden layer feed forward network (SLFN) with at most N hidden
nodes and with almost any nonlinear activation function can exactly learn N
distinct observations. According to conventional neural network theories,
SLFN with additive or RBF hidden nodes are universal approximations when
all the parameters of the networks are adjustable. However, as observed in
most neural network implementations, tuning all the parameters of the
networks may result in complicated and inefficient learning, and difficult to
train networks with non-differentiable activation functions such as threshold
networks. [Huang et.al, 2006] It is proved that the input weights and hidden
layer biases of SLFN can be randomly assigned if the activation functions in
the hidden layer are infinitely differentiable [Huang et.al, 2004a].
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188 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
A new machine learning algorithm called ELM for SLFNs is presented in
this section. In ELM, input weights and hidden layer biases are randomly
chosen, and they need not be adjusted at all. This method not only makes the
learning extremely fast but also produces good generalization performance.
After parameters are chosen randomly, SLFN can be simply considered as a
linear system and the output weights of SLFN can be analytically determined
through simple generalized inverse operation of the hidden layer output matrices.
Based on this concept, we explore ELM whose learning speed can be thousands
of times faster than traditional feed forward neural network learning algorithms
like Multi-Layer perceptron or back propagation [Chacko et.al, 2012] [Huang et
.al, 2006] [Huang and Babri, 1998].
The GLCM is second order statistical feature depend on the texture
pattern of the ROIs based on neighboring pixels. The neighboring pixels
distributions extracted in four different orientations of the image constitute
the texture pattern in the form of gray level variation in the image. From the
GLCM four important features viz. Contrast, Energy, Homogeneity and
Correlations are obtained. The four features in four different orientations are
combined together to form a feature vector as explained in section 3.4.5. This
acts as the input for MLP and ELM.
For Wavelet features, multilevel DWT is applied on the ROIs
extracted from mammogram images. Three level decomposition of wavelet
transformation coefficients are carried out by using different wavelet families
such as Daubechies, Biorthoganl and Haar. All the approximation
coefficients in each level of the DWT can be used as the feature set. But the
transformation coefficients generated on the multilevel decomposition of the
transformation results in high dimensional feature vector. This makes the
classification process too difficult to manage. So the dimension reduction
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 189
methods are applied to select the most significant subset from the
approximation coefficient sets. For this PCA is applied on each level of the
approximation coefficients. The reduced wavelet transformation coefficients
obtained after the PCA analysis in the form of Eigen values and Eigen matrix
in which highest Eigen values of the principal component are taken as the
feature set. This feature is then fed as the input to the MLP and ELM
machine learning algorithms.
6.6.2 Results and Discussion
Using MLP and ELM, we classified all the benign and malignant
images in the Mini-MIAS dataset. We extracted 116 abnormal mammogram
ROIs which contain 64 benign and 52 malignant images based on the
segmentation algorithm discussed in chapter 5. The feature extraction
methods are applied on these ROIs to form the feature vector and given as
inputs to MLP and ELM.
The classification result obtained by the Multi-Layer Perceptron
(MLP) is shown in Table 6.36. The table reveals the performance of MLP in
terms of the classification accuracy as well as average performance time. The
MLP proposed here used 10 fold cross validation for training and testing the
dataset. One of the important remark regarding this classification is that the
both the GLCM and wavelet based classification achieved 90.52%
classification accuracy with 8.92 and 5.18 seconds respectively. By
comparing the different types of wavelet filters, it is observed that the
classification accuracy obtained with db4 filter is far better than all the
wavelet filters considered.
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190 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
Table 6.36: Performance of the classification of mammogram images using ANN
Feature set Accuracy (%) Time for Training (seconds)
No. hidden nodes Epoch
GLCM
Wavelets
db4
db8
db16
Haar
Biorthogonal
90.52
90.52
86.21
87.10
81.90
79.31
8.92
5.18
5.28
5.01
4.89
3.15
17
17
17
17
17
17
1500
1750
1750
1750
1500
1000
The ELM algorithm determines SLFN parameters randomly or
experimentally. The 116 mammogram ROIs (64 benign and 52 malignant)
images extracted from the Mini-MIAS database are divided randomly into
training and testing sets in the ratio 9:1. The number of hidden neurons is
always set to a value less than the total samples used in the training set. The
results obtained by GLCM and wavelet based feature sets are shown in Table
6.37. The table reveals that classification accuracy obtained by ELM using
GLCM feature is better than the wavelet based feature sets (83.88%). As far
as wavelet filters are concerned, db4 filter outperform others. By comparing
the performance of the classification mammogram ROIs using MLP and
ELM, it is evident that MLP based classification with GLCM and Wavelet
features gives better result than the ELM. On the other hand MLP consumes
more time than ELM for training. The Figure 6.40 shows the performance of
the two classifiers in classifying abnormal mammogram images into benign
and malignant.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 191
Table 6.37: Performance of the classification of mammogram images using ELM
Feature Accuracy in (%)
Time for Training (sec) No. Hidden Neurons
GLCM 83.88 0.0624 16
Wavelet
db4
db8
db16
Haar
Biorthogonal
81.00
79.19
78.74
71.40
72.40
0.0936
0.0936
0.0624
0.0624
0.0624
28
28
28
18
18
Figure 6.40 : Classification performance of mammogram images using ANN and ELM.
6.7 Comparative Analysis of Different Approaches and Algorithms
Used for Classifying Mammogram Images
Finally we conclude this chapter with the analysis of overall
performance of different classifiers for classifying the mammogram images
into primary risk level using two different feature sets. Wavelet
transformation coefficients as well as Gray Level Co-occurrence Matrices are
used as the two prominent features throughout this work. Approximation
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ANN
ELM
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192 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images
coefficients obtained in the WT as well as Contrast, Energy, Homogeneity
and Correlation derived from GLCM in different orientations is used as the
feature set for the classification. In addition to this, a reduced wavelet
transformation coefficient is also used as the reduced feature set for certain
classifiers. The classifiers such as distance measure, Multilayer Perceptron
(MLP), Extreme Learning Machine (ELM) and Lazy classifiers are used for
classifying the mammogram images. The overall classification accuracy
obtained by these classifiers is shown in Table 6.38.
Table 6.38: Overall classification accuracy obtained by various classifiers using DWT and GLCM feature
Feature set Overall performance of different classifiers in % Distance Measure MLP ELM Lazy
Wavelet (DWT) 82.72 90.52* 81.00* 100*
GLCM 82.72 90.52 83.88 96.96 *wavelet coefficients after PCA
Both Wavelet transformation coefficients and GLCM feature have
obtained same classification accuracy (82.72%) using the Euclidean distance
measure. Using MLP, both feature set produced 90.52% of classification. But
the MLP classifier used reduced wavelet transformation coefficients as the
feature set. ELM, a recent addition to neural network classifier produced
81% of classification accuracy on reduced DWT coefficients and 83.88% for
GLCM feature set. Though offer faster training, classification performance of
ELM is inferior to MLP. Finally the Lazy classifiers that classified the
mammogram images with 96.96 % accuracy in overall classification for
GLCM feature set and 100% classification accuracy for reduced wavelet
transformation coefficients for mammogram images in the Mini-MIAS
dataset. The performance of the above classifier is plotted in Figure 6.41. The
study reveals that feature vector obtained with PCA reduced DWT
coefficients and Lazy classifiers (K*/IBL) is the best alternative.
Classification Algorithms for Detection of Abnormalities in Mammogram Images
Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 193
Figure 6.41: Overall performance of the classification of mammogram images using DWT and
GLCM feature set on different classifiers
6.8 Summary
In this chapter we implemented different classification algorithms for
classifying mammogram images into different categories based on the two
different feature sets. The classification is carried out with different
classifiers such as distance measure, Multilayer Perceptron, Extreme
Learning Machine and Lazy classifiers. Wavelet Transformation coefficients
and Gray Level Co-occurrence Matrix are used as the feature set. Out of four
different classification algorithms, Multilayer Perceptron and Lazy classifiers
are implemented using WEKA data mining software and other classifiers are
implemented using MATLAB software. Extensive study is carried out with
the chosen feature sets and classifiers. Based on the classification accuracy,
sensitivity and specificity PCA reduced DWT coefficients based features and
Lazy classifiers (K*/IBL) are found to be the best alternative among the
different classifiers we experimented with.
…….…….
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Wavelet (DWT)
GLCM