CLASSIFICATION ALGORITHMS FOR DETECTION OF...

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.

Chapter 6

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

Chapter 6


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



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


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



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


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)



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.

Chapter 6


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




Table 6.3: Classification of mammogram images using Daubechies filters in SWT (Bray Curtis)

Coef. In %

Daubechies Db4 Db8 Db16

25


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

Chapter 6


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




Table 6.7: Successful classification rate (in %) of mammogram images using discrete stationary Daubechies filters (Bray Curtis)

Coef. In %

Db4 Db8 Db16



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 %


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


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



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


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.



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


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

Benign

Malignant

Overall

Chapter 6



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%


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


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

benign

Malignant

Overall

0102030405060708090


Clas

sifica

tion

perf

orm

ance

in%

Normal

Benign

Malignant

Overall



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%


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


Figure 6.5: Classification performance of mammogram images using 25% wavelet transofrmation coefficients ( Bray Curtis distance)


coefficients (Bray Curtis Distance)



0102030405060708090


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

Benign

Malignant

overall

0102030405060708090


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

Benign

Malignant

overall

0102030405060708090


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

Benign

Malignant

overall





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


Clas

sific

atio

n pe

rfor

man

ce in

%

Normal

Benign

Malignant

overall

Chapter 6


Table 6.11: Classification of mammogram images using Daubechies filter in DWT (Euclidean)

Coef. in %

Wavelet Filter : Daubechies db4 db8 db16

25


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



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


Chapter 6


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



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


Table 6.16: Successful classification rate (in %) of mammogram images using Haar & Biorthogonal discrete wavelet decomposition (Euclidean)

Coef. In %


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


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




Table 6.18:-Successful classification rate (in %) of mammogram images using discrete Haar & Biorthogonal wavelet decomposition (Bray Curtis)

Coef. In %


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


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: Bio


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


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





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


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


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.



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)



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Chapter 6






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%.

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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.


transofrmation coefficients (Bray Curtis)

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Chapter 6








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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

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Chapter 6


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)

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Stationary Wavelet Transformation Discrete Wavelet Transformation

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Chapter 6



Figure 6.24: Classification performance of Haar wavelet (Euclidean Distance)

Figure 6.25: Classification performance of Haar Wavelet(Bray Curtis)

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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

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100


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Chapter 6


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



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


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


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



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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Chapter 6


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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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

Chapter 6


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.



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.


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

Chapter 6


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




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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LWL

Chapter 6


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

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8x8

16x16

32x32

64x64



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


Chapter 6


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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K*

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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



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



s. The confusion matrices generated by the

Specificity

8x8

16x16

32x32

64x64

Chapter 6

176


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



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

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Kstar

IBL

LWL

Chapter 6


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



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

Chapter 6


lazy learning classifiers K*, IBL and LWL are used for classificatio

diagram of the proposed system is shown in

Fig.6.36: Block diagram


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


n. The block

of the mammogram image classification using PCA and Lazy classifiers




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

Chapter 6


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

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K*

IB1

LWL



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.

Chapter 6


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

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Accuracy



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

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Accuracy (%)

Chapter 6


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



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].

Chapter 6


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



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.


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.

Chapter 6


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.



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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perf

orm

ance

in%

ANN

ELM

Chapter 6


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.



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.

…….…….

0102030405060708090

100

Distance MLP ELM Lazy

Clas

sifica

tion

perf

orm

ance

in%

Wavelet (DWT)

GLCM

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