Ms thesis-final-defense-presentation
-
Upload
nashid-alam -
Category
Education
-
view
545 -
download
1
Transcript of Ms thesis-final-defense-presentation
Computer Assisted Screening of Microcalcifications in Digitized Mammogram for Early Detection of Breast CancerThesis Presentation
Nashid AlamRegistration No: [email protected]
Supervisor: Prof. Dr. Mohammed Jahirul Islam
Department of Computer Science and EngineeringShahjalal University of Science and Technology
Driving research for better breast cancer treatment “The best protection is early detection”
010
2030
020
40-0.1
0
0.1
5; 0.5; 0.7854
5; 0.5; 0.7854
Thursday, December 23, 2015
Introduction
Breast cancer:The most devastating and deadly diseases for women.
o Computer aided detection (CADe) o Computer aided diagnosis (CADx) systems
We will emphasis on :
Background Interest
Background Interest
Interest comes from two primary backgrounds
1. Improvement of pictorial information- - For Human Perception
How can an image/video be made more aesthetically pleasing
How can an image/video be enhanced to facilitate:extraction of useful information
Background Interest
Interest comes from two primary backgrounds
2. Processing of data for:Autonomous machine perception- Machine Vision
Micro-calcification
Mammography
Mammogram
Micro-calcification
Background knowledge
Micro-calcification
Micro-calcifications :- Tiny deposits of calcium- May be benign or malignant- A first cue of cancer.
Position:1. Can be scattered throughout the mammary gland, or 2. Occur in clusters.(diameters from some µm up to approximately 200 µm.)3. Considered regions of high frequency.
Micro-calcification
They are caused by a number of reasons:
1. Aging –The majority of diagnoses are made in women over 50
2. Genetic –Involving the BRCA1 (breast cancer 1, early onset) and
BRCA2 (breast cancer 2, early onset) genes
Micro-calcifications Pattern Determines :The future course of the action-
I. Whether it be further investigatory techniques (as part of the triple assessment), or
II. More regular screening
Mammography
Background knowledge
Mammography Machine
Mammography
USE:I. Viewing x-ray imageII. Manipulate X-ray image on a computer screen
Mammography :
Process of using low-energyx-rays to examine the human breast
Used as a diagnostic and a screening tool.
The goal of mammography :The early detection of breast cancer
Mammography Machine
Mammogram
Background knowledge
mdb226.jpg
Mammogram
Mammogram:An x-ray picture of the breast
Use:To look for changes that are not normal.
Result Archive:The results are recorded:
1. On x-ray film or 2.Directly into a computer
mdb226.jpg
Literature Review
To detect micro-calcifications:-A number of methods have been proposed
These include:
Global and local thresholding
Statistical approaches
Neural networks
Fuzzy logic
Thresholding of wavelet coefficients and related techniques.
Literature Review
Focus:Preprocessing Techniques of Mammogram:
Goals:- Pectoral mussel identification- Noise removal- Image enhancement
-No method gives full satisfaction andclinically acceptable results.
Drawbacks:
Literature Review
local range modification algorithmIntegrated wavelets form MC modelStein's thresholding [7]for denoisingUse Contourlets
Method used:Watershed transformationBoundary based methodHybrid techniquesThresholding techniques
Heinlein et.al(2003) [1]Zhibo et.al.(2007) [2]Papadopoulus et al. (2008) [3]
Razzi et al.(2009) [4]Pronoj et al.(2011) [5]Camilus et al.(2011) [6]
Focus:Local feature extraction of Mammogram:
Pal et al.(2008) [8]Yu et al. (2010) [9]
Oliver et al.(2010) [10]
Goals:
- Detect microcalcification and MC cluster
- Only deals with MC morphology
-Position of microcalcifications(Take into account)
-To segment mammogram:Only salient fracture are computed
Drawbacks:
Literature Review
Method used:Inspect local neighborhood of each MCWeighted density functionFuzzy Shell Clustering
Training stage: Pixel-based boosting classifierMulti-layered perception networkBack propagated neural network
Balakumaran et.al.(2010) [11] Oliver et al.(2012) [12]Zhang et.al.(2013)[13]
Literature Review
Focus:Wavelet based Techniques
Wang et.al.(1989) [14]Daubechies I.(1992) [15]Strickland et.at (1996) [16]Papadopoulus et al. (2008) [3]
Goals:
Method used:two-stage decomposition wavelet filteringdiscrete wavelet linear stretching and shrinkage algorithm.low-frequency subbands are discardedbiorthogonal filter bank used
Drawbacks:
-Cluster was considered:if more then 3 microcalcifications
were detected in a 1cm2 area
- Detect microcalcification and MC cluster
Razzi et al.(2009) [4]Yu et al.(2010) [9]Balakumaran et.al.(2010) [11]Zhang et.al.(2013) [13]
Literature Review
Focus:Analysis of large massesinstead of microcalcifications
Zhibo et.al.(2007)[2]Lu et.al.(2013) [17]
Goals:Drawbacks:
Method used:
Mass Detection
Multiscale regularized reconstructionHybrid Image Filtering MethodNoise regularization in DBT reconstructionUse Contourlets
- Detecting subtle mass lesionsin Digital breast tomosynthesis (DBT)
- Only detect large mass
Digital BreastTomosynthesis captures
PHOTO COURTESY :http://www.itnonline.com/article/trends-breast-imaginghttp://www.hoag.org/Specialty/Breast-Program/Pages/breast-screening/screening-types/Tomosynthesis.aspx
Literature Review
Focus:Detect /Classify mammograms
Fatemeh et.al.(2007) [18]
Goals:
Drawbacks:
Method used:
Automatic mass classification
Contourlets Transform
Does not give full satisfaction andclinically acceptable results.
PHOTO COURTESY :https://www.youtube.com/watch?v=kRwKO5k6pi
Mammogram
Literature Review
Focus:
Template matching algorithm
Leeuw et.al.(2014) [7]
Goals: Drawbacks:
Method used:
Detect microcalcifications in breast specimens
Phase derivative to detect microcalcifications
Used MRI instead of mammogram
Breast MRIBreast MRI Machine
PHOTO COURTESY :http://www.leememorial.org/mainlanding/Breast_mri.asp
Literature Review
Focus:
Goals:Insertion of simulated microcalcification clusters:
- In a software breast phantom
PHOTO COURTESY :http://www.math.umaine.edu/~compumaine/index.html
Left: Cluster microcalcification in breast tissue. Right: Simulated cluster microcalcification.
-Algorithm developed as part ofa virtual clinical trial (VCT) :
-Simulation of breast anatomy, - Mechanical compression- Image acquisition- Image processing- Image displaying and interpretation.
Shankla et.al.(2014)[19]
Problem Statement
Burdensome Task Of Radiologist : Eye fatigue:
-Huge volume of images-Detection accuracy rate tends to decrease
Non-systematic search patterns of humansPerformance gap between :
Specialized breast imagers andgeneral radiologists
Interpretational Errors:Similar characteristics:
Abnormal and normal microcalcification
Problem Statement
Reason behind the problem( In real life):
The signs of breast cancer are:
Masses CalcificationsTumorLesionLump
Individual Research Areas
Problem Statement
Motivation to the Research
Motivation to the research: Goal
Better Cancer Survival Rates(Facilitate Early Detection ).
Provide “second opinion” : Computerized decisionsupport systems
Fast,Reliable, andCost-effective
Overcome:The development of breast cancer
Challenges
Develop a logistic model:
Feature extraction Challenge:
-To determine the likelihood of CANCEROUS AREA -- From the image values of mammograms
Challenge:Occur in clusters
The clusters may vary in size from 0.05mm to 1mm in diameter.
Variation in signal intensity and contrast.May located in dense tissue
Difficult to detect.
Challenges
Materials and Tools
Matlab 2014
Database: mini-MIAS
Database: mini-MIAS databasehttp://peipa.essex.ac.uk/pix/mias/
Class of Abnormality
Severity of Abnormality
The Location of The
Center of The
Abnormality and It’s
Diameter.
1 Calcification(25)
1.Benign(Calc-12)
2 Circumscribed Masses
3 Speculated Masses
4 Ill-defined Masses
5 Architectural Distortion
2.Malignant(Cancerous)
(Calc-13)
6 Asymmetry
7Normal
mdb223.jpg mdb226.jpg
mdb239.jpg mdb249.jpg
Figure01:X-ray image form mini-MIAS database
Database: Mini-MIAS Databasehttp://peipa.essex.ac.uk/pix/mias/
Mammography Image Analysis Society (MIAS) -An organization of UK research groups
• Consists of 322 images-- Contains left and right breast images for 161 patients
• Every image is 1024 X 1024 pixels in size
• Represents each pixel with an 8-bit word
• Reduced in resolution(Is not good enough for MC to be detectable)
•Very Poor Quality with .jpg compression effects(Original MIAS doesn’t have such artifacts)
Mini-MIAS Database
Mammography Image Analysis Society (MIAS) -An organization of UK research groups
Database: http://peipa.essex.ac.uk/pix/mias/
http://see.xidian.edu.cn/vipsl/database_Mammo.html
Plan of Action
Where Are We? Our Current Research Stage
Thesis SemesterM-3
Chart 01: Gantt Chart of this M.Sc thesis Showing the duration of task against the progression of time
Where Are We? Our Current Research Stage
Thesis SemesterM-3
Schematic representation of the system
Sche
mat
ic r
epre
sent
atio
n of
the
syst
em
Removing Pectoral MuscleAnd
X-ray Label
X-ray Label Removing Finding The Big BLOB
The types X-ray Label:High Intensity Rectangular LabelLow Intensity LabelTape Artifacts
X-ray Label Removing
1. Histogram equalization of the original X-ray image
2. Adjust image contrast
3. Apply Otsu's Thresholding Method [20] and
find bi-level the image which has several blobs in it.
4. Finding the Largest blob (Bwlargest.bolb)
5. Hole filling within the blob region
6. Keep the true pixel value covering only the area of largest blob and discard other features from the original image
7. X-ray label is successfully removed
Plan of Action
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
To Achieve The Desired Final Result:Apply:
A Range Of Techniques on original image
1.Original image
2.HistogramEqualization
3.Contrast Image
4.Binary Image
mdb239.jpg
Combining Range of techniques
J = histeq(I); %histogram equalization
contrast_image = imadjust(J, stretchlim(J), [0 1]); %high contrast image
%Apply Thresholding to the Image level = graythresh(contrast_image);
%GRAYTHRESH Global image threshold using %Otsu's methodbw_image = im2bw(contrast_image, level);%getting binary image
X-ray Label Removing
5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
Combining Range of techniquesX-ray Label Removing
Result image(Label Removed)
Original image
Compare the original and final image
X-ray Label Removing
Experimental results
X-ray Label Removing
X-ray Label Removing1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb212.jpg
mdb214.jpg
mdb214.jpg
mdb218.jpg
mdb219.jpg
Benign
X-ray Label Removing Benign1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb222.jpg
mdb223.jpg
mdb226jpg
mdb227jpg
X-ray Label Removing Benign1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb226.jpg
mdb240.jpg
mdb248.jpg
mdb252.jpg
X-ray Label Removing Malignant1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb209.jpg
mdb211.jpg
mdb213.jpg
mdb216.jpg
mdb231.jpg
X-ray Label Removing Malignant1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb233.jpg
mdb238.jpg
mdb239.jpg
mdb241.jpg
X-ray Label Removing Malignant1.Original image
2.HistogramEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole fillingInside the blob
7.Result image(Label Removed)
mdb245.jpg
mdb249.jpg
mdb253.jpg
mdb256.jpg
Successful
X-ray Label Removing
Finally!
Removing pectoral muscleKeeping fatty tissues and ligaments
mdb212.jpg(a)Main Image (b)Result Image
mdb213.jpg(a)Main Image (b)Pectoral Muscle
mdb214.jpg
Main Image
Result Image
o Fat t y t i s s ue are ao Duc to Lobul e so Si nuso l i gam e nt s
Extraction of ROIRemoving pectoral muscle
Why removing pectoral muscle?
o Pectoral muscle will never contain micro-calcification
o Less Computational Time And Cost-Operation on small image area
Existence of micro-calcification:
ROI
Edge Detection of pectoral muscleRemoving pectoral muscle
Points to be noted :
-Pectoral muscle a Triangular areamdb212.jpg
mdb214.jpg
Based on this point: Moving on towards solution
mdb209.jpg
(2)Binary Image(1)Original Image
Triangle Detection of pectoral muscleRemoving pectoral muscle
1. Find the triangular area of the pectoral muscle region
I. Finding white seeding pointII. Finding the 1st black point of 1st row after getting a white seeding pointIII. Draw a horizontal line in these two points.IV. finding the 1st black point of 1st column after getting a white seeding pointV. Draw a vertical line and angular line.
2. Making all the pixels black(zero)resides in the pectoral muscle area
Triangle Detection of pectoral muscle
Visualization in next slide
Triangle Detection of pectoral muscleRemoving pectoral muscle
Approach-03(Triangle Detection of pectoral muscle):
mdb212.jpg1.Original image
2.Contrast stretching
3.Binary of contrast image
stratching_in_range=uint8(imadjust(I,[0.01 0.7],[1 0]));
BW=~stratching_in_range;
Triangle Detection of pectoral muscleRemoving pectoral muscle
Approach-03(Triangle Detection of pectoral muscle):
4.Triangle
5.Triangle Filled
6.muscle removed
Experimental results
Removing pectoral muscleApproach-03(Triangle Detection of pectoral muscle):
Triangle Detection of pectoral muscle
Triangle Detection of pectoral muscleRemoving pectoral muscle
mdb212.jpg
mdb214.jpg
1.Original image 2.Contrast stretching 3.Binary of contrast image 4.Triangle
mdb240.jpg
mdb248.jpg
5.Triangle Filled 6.muscle removed
Class: Benign
Triangle Detection of pectoral muscleRemoving pectoral muscle
mdb222.jpg
mdb226.jpg
mdb227.jpg
2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle
Problems faced
5.Triangle Filled 6.muscle removed
The triangle does not always indicates the proper pectoral muscle area.Reason:
Class: Benign
Artifacts in mammogram
2.Contrast stretching1.Original image
3.Binary of contrast image 4.Triangle
5.Triangle Filled 6.muscle removed
Triangle Detection of pectoral muscleRemoving pectoral muscle
Problems faced:
Defects in mammogram (Vertical Stripe Missing)
mdb227.jpg
Class: Benign
mdb223.jpg
2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed
Triangle Detection of pectoral muscleRemoving pectoral muscle
Problems faced:
Defects in mammogram (Horizontal Stripe Missing)Solution: Replicate the 2nd and 3rd row)
Class: Benign
Triangle Detection of pectoral muscleRemoving pectoral muscle
1.Original image
2.Contrast stretching
3.Binary of contrast image
4.Triangle
5.Triangle Filled
6.muscle removed
Class: Malignant
mdb256.pg
Triangle Detection of pectoral muscleRemoving pectoral muscle
mdb212.jpg
mdb214.jpg
1.Original image 2.Contrast stretching 3.Binary of contrast image 4.Triangle
mdb240.jpg
mdb248.jpg
5.Triangle Filled 6.muscle removed
Class: Benign
Successful
Pectoral Muscle Removing
Finally!
Improved Computer Assisted Screening
Enhancement of digitized mammogram
Goal
MAIN NOVELTY
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Based on the classical approach used in transform methods for image processing.
1. Input mammogram
2. Forward CT
3. Subband Processing
4. Inverse CT
5. Enhanced Mammogram
Schematic representation of the system
Contourlet transformation
Implementation Based On :
• A Laplacian Pyramid decomposition followed by -
• Directional filter banks applied on each band pass sub-band.
The Result Extracts:-Geometric information of images.
Details in upcoming slides
Main Novelty
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Why Contourlet?
Why Contourlet?
•Decompose the mammographic image:-Into directional components:
To easily capture the geometry of the image features.
Details in upcoming slides
Target
Enhancement of the Directional Subbands
The Contourlet TransformLaplacian Pyramid: 3 level
DecompositionFrequency partitioning of a directional filter bank
Decomposition level l=3
The real wedge-shape frequency band is 23=8.
horizontal directions are corresponded by sub-bands 0-3
Vertical directions are represented by sub-bands 4-7
Details in upcoming slides
Enhancement of the Directional Subbands
The Contourlet TransformLaplacian Pyramid: 3 level
Decomposition
Laplacian Pyramid Level-1
Laplacian Pyramid Level-2
Laplacian Pyramid Level-3
8 Direction
4 Direction
4 Direction
(mdb252.jpg)
Enhancement of the Directional Subbands
The Contourlet TransformLaplacian Pyramid: 3 level
Decomposition
Wedge-shape frequency band is 23=8.
Horizontal directions are corresponded by sub-bands 0-3
(1) sub-band 0
(2) sub-band 1
(3) sub-band 2(4) sub-band 3
Contourlet coefficient at level 4
Enhancement of the Directional Subbands
The Contourlet TransformLaplacian Pyramid: 3 level
DecompositionContourlet coefficient at level 4
Wedge-shape frequency band is 23=8.
Vertical directions are represented by sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7
Enhancement of the Directional Subbands
The Contourlet TransformLaplacian Pyramid: 3 level
Decomposition
(a) Main Image(mdb252.jpg)
(b) Enhanced Image(Average in all 8 direction)
(a) Main image(Toy Image)
Contourlet Transform Example
(b) Horizontal Direction
(c) Vertical Direction
Directional filter banks: Horizontal and Vertical
Contourlet Transform ExampleDirectional filter banks
Horizontal directions are corresponded by sub-bands 0-3
(1) sub-band 0
(2) sub-band 1
(3) sub-band 2
(4) sub-band 3
Contourlet Transform ExampleDirectional filter banks
Vertical directions are represented by sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Plan-of-Action
For microcalcifications enhancement :
We use-The Contourlet Transform(CT) [21]
The Prewitt Filter.
21. Da Cunha A. L., Zhou J. and Do M. N,: The Nonsubsampled Contourlet Transform: Theory, Design, and Applications, IEEE Transactions on Image Processing,vol. 15, (2006) pp. 3089-3101
Art-of-Action
An edge Prewitt filter to enhance the directional structures
in the image.
Contourlet transform allows decomposing the image in
multidirectional and multiscale subbands[22].
22. Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement by multiscale analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994) pp. 7250-7260
This allows finding • A better set of edges,• Recovering an enhanced mammogram with better visual characteristics.
Microcalcifications have a very small size a denoising stage is not implemented
in order to preserve the integrity of the injuries.
Decompose the digital mammogram
Using Contourlet transform
(b) Enhanced image(mdb238.jpg)
(a) Original image (mdb238.jpg)
The Contourlet Transform
The CT is implemented by:Laplacian pyramid followed by directional filter banks (Fig-01)
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Figure 01: Structure of the Laplacian pyramid together with the directional filter bank
The concept of wavelet:University of Heidelburg
The CASCADE STRUCTURE allows:- The multiscale and
directional decomposition to be independent
- Makes possible to:Decompose each scale into
any arbitrary power of two's number of directions(4,8,16…)
Figure 01
Details ………….
Decomposes The Image Into Several Directional Subbands And Multiple Scales
Figure 02: (a)Structure of the Laplacian pyramid together with the directional filter bank(b) frequency partitioning by the contourlet transform(c) Decomposition levels and directions.
(a) (b)
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Details….
(c)
DenoteEach subband by yi,j
Wherei =decomposition level and J=direction
The Contourlet TransformDecomposes The Image Into Several Directional Subbands And Multiple Scales
The processing of an image consists on:-Applying a function to enhance the regions of
interest.
In multiscale analysis:
Calculating function f for each subband :-To emphasize the features of interest-In order to get a new set y' of enhanced subbands:
Each of the resulting enhanced subbands can be expressed using equation 1.
)(', , jiyfjiy = ………………..(1)
-After the enhanced subbands are obtained, the inverse transform is performed to obtain an enhanced image.
Enhancement of the Directional Subbands
The Contourlet Transform
Denote
Each subband by yi,jWherei =decomposition level and J=direction Details….
Enhancement of the Directional Subbands
The Contourlet Transform
Details….
The directional subbands are enhanced using equation 2.
=)( , jiyf)2,1(
,1 nnWjiy
)2,1(,2 nnWjiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1………..(2)
Denote
Each subband by yi,jWherei =decomposition level and J=direction
W1= weight factors for detecting the surrounding tissueW2= weight factors for detecting microcalcifications
(n1,n2) are the spatial coordinates.
bi;j = a binary image containing the edges of the subband
Weight and threshold selection techniques are presented on upcoming slides
Enhancement of the Directional Subbands
The Contourlet Transform
The directional subbands are enhanced using equation 2.
=)( , jiyf)2,1(
,1 nnWjiy
)2,1(,2 nnWjiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1………..(2)
Binary edge image bi,j is obtained :-by applying : Prewitt edge detector
-To detect edges on each directional subband.
In order to obtain a binary image:A threshold Ti,j for each subband is calculated.
Details….
Weight and threshold selection techniques are presented on upcoming slides
Threshold Selection
The Contourlet Transform
Details….
The microcalcifications appear :
On each subband Over a very
homogeneous background.
Most of the transform coefficients:
-The coefficients corresponding to theinjuries are far from background value.
A conservative threshold of 3σi;j is selected:where σi;j is the standard deviation of the corresponding subband y I,j .
Weight Selection
The Contourlet Transform
Exhaustive tests:-Consist on evaluating subjectively a set of 322 different mammograms
-With Different combinations of values,
The weights W1, and W2 are determined:- as W1 = 3 σi;j and W2 = 4 σi;j
These weights are chosen to:keep the relationship W1 < W2:
-Because the W factor is a gain -More gain at the edges are wanted.
Experimental Results
Applying Contourlet Transformation Benign
Original image Enhanced image
Goal: Microcalcification Enhancement
mdb222.jpg
mdb223.jpg
Original image Enhanced image
mdb248.jpg
mdb252.jpg
Applying Contourlet Transformation Benign
Original image Enhanced image
mdb226.jpg
mdb227.jpg
Original image Enhanced image
mdb236.jpg
mdb240.jpg
Goal: Microcalcification Enhancement
Applying Contourlet Transformation Benign
Original image Enhanced image Original image Enhanced image
mdb218.jpgmdb219.jpg
Goal: Microcalcification Enhancement
Applying Contourlet Transformation MalignantGoal: Microcalcification Enhancement
Original image Enhanced image
mdb209.jpg
mdb211.jpg
Original image Enhanced image
mdb213.jpg
mdb231.jpg
Applying Contourlet Transformation MalignantGoal: Microcalcification Enhancement
Original image Enhanced image
mdb238.jpg
mdb239.jpg
Original image Enhanced image
mdb241.jpg
mdb249.jpg
Original image Enhanced image
mdb253.jpg
Original image Enhanced image
Applying Contourlet Transformation MalignantGoal: Microcalcification Enhancement
mdb256.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb003.jpg
mdb004.jpg
Original image Enhanced image
mdb006.jpg
mdb007.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb009.jpg
mdb018.jpg
Original image Enhanced image
mdb027.jpg
mdb033.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb046.jpg
mdb056.jpg
Original image Enhanced image
mdb060.jpg
mdb066.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb070.jpg
mdb073.jpg
Original image Enhanced image
mdb074.jpg
mdb076.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb093.jpg
mdb096.jpg
Original image Enhanced image
mdb101.jpg
mdb012.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb128.jpg
mdb137.jpg
Original image Enhanced image
mdb146.jpg
mdb154.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb166.jpg
mdb169.jpg
Original image Enhanced image
mdb224.jpg
mdb225.jpg
Applying Contourlet Transformation NormalGoal: Microcalcification Enhancement
Original image Enhanced image
mdb263.jpg
mdb294.jpg
Original image Enhanced image
mdb316.jpg
mdb320.jpg
Wavelet Transformation
Use Separable Transform
2D Wavelet Transform
Visualization
Label ofapproximation
HorizontalDetails
HorizontalDetails
VerticalDetails
DiagonalDetails
VerticalDetails
DiagonalDetails
Use Separable Transform
2D Wavelet Transform
Decomposition at Label 4
Original image(with diagonal details areas indicated)
Diagonal Details
Use Separable Transform
2D Wavelet Transform
Vertical Details
Decomposition at Label 4
Original image(with Vertical details areas indicated)
Experimental Results
Experimental Results
DWT
1.Original Image(Malignent_mdb238) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
Experimental Results
DWT
1.Original Image(Malignent_mdb238) 2.Decomposition at Label 4
Experimental Results
1.Original Image(Benign_mdb252) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
DWT
Experimental Results
1.Original Image(Malignent_mdb253.jpg) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
Metrics: Quantitive Measurement
Metrics
To compare the ability of :Enhancement achieved by the proposed method
Why?
1. Measurement of distributed separation (MDS)2. Contrast enhancement of background against target (CEBT) and3. Entropy-based contrast enhancement of background against target (ECEBT) [23].
Measures used to compare:
23. Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques for Mammographic Breast Masses, IEEETransactions on Information Technology in Biomedicine, vol. 9, (2005) pp. 109-119
Metrics
1. Measurement of Distributed Separation (MDS)
Measures used to compare:
The MDS represents :How separated are the distributions of each mammogram
…………………………(3)MDS = |µucalcE -µtissueE |- |µucalc0 -µtissue0 |
µucalcE = Mean of the microcalcification region of the enhanced imageµucalc0 = Mean of the microcalcification region of the original image
µtissueE = Mean of the surrounding tissue of the enhanced imageµtissue0 = Mean of the surrounding tissue of the enhanced image
Defined by:
Where:
Metrics
2. Contrast enhancement of background against target (CEBT) Measures used to compare:
The CEBT Quantifies :The improvement in difference between the background and the target(MC).
…………………………(4)
0µucalcEµucalc
0µtissue0µucalc
EµtissueEµucalc
CEBT
σσ
−=
Defined by:
Where:
Eµucalcσ
0µucalcσ
= Standard deviations of the microcalcifications region in the enhanced image
= Standard deviations of the microcalcifications region in the original image
Metrics
3. Entropy-based contrast enhancement of background against target (ECEBT)Measures used to compare:
The ECEBT Measures :- An extension of the TBC metric- Based on the entropy of the regions rather
than in the standard deviations
Defined by:
Where:
…………………………(5)
0µucalcEµucalc
0µtissue0µucalc
EµtissueEµucalc
ECEBT
εζ
−=
= Entropy of the microcalcifications region in the enhanced image
= Entropy of the microcalcifications region in the original image
Eµucalcζ
0µucalcε
Experimental Results
MDS, CEBT and ECEBT metrics on the enhanced mammograms
Experimental Results
CT Method DWT Method
MDS CEBT ECEBT MDS CEBT ECEBT0.853 0.477 0.852 0.153 0.078 0.555
0.818 0.330 0.810 0.094 0.052 0.382
1.000 1.000 1.000 0.210 0.092 0.512
0.905 0.322 0.920 1.000 0.077 1.000
0.936 0.380 0.935 0.038 0.074 0.473
0.948 0.293 0.947 0.469 0.075 0.847
0.665 0.410 0.639 0.369 0.082 0.823
0.740 0.352 0.730 0.340 0.074 0.726
0.944 0.469 0.494 0.479 0.095 0.834
0.931 0.691 0.936 0.479 0.000 0.000
0.693 0.500 0.718 0.258 0.081 0.682
0.916 0.395 0.914 0.796 0.079 0.900
Table 1. Decomposition levels and directions.
0
0.2
0.4
0.6
0.8
1
1.2
TBC
Mammogram
MDS Matrix
CT DWT
The proposed method gives higher results than the wavelet-based method.
MDS, CEBT and ECEBT metrics on the enhanced mammograms
Experimental Results Analysis
0
0.2
0.4
0.6
0.8
1
1.2
TBCE
Mammogram
CEBT Matrix
CT DWT
The proposed method gives higher results than the wavelet-based method.
MDS, CEBT and ECEBT metrics on the enhanced mammograms
Experimental Results Analysis
0
0.2
0.4
0.6
0.8
1
1.2
DSM
Mammogram
ECEBT Matrix
CT DWT
The proposed method gives higher results than the wavelet-based method.
MDS, CEBT and ECEBT metrics on the enhanced mammograms
Experimental Results Analysis
Experimental Results AnalysisMesh plot of a ROI containing microcalcifications
(a)The original mammogram
(mdb252.bmp)
(b) The enhanced mammogram
using CT
Experimental Results Analysis
(a)The original mammogram
(mdb238.bmp)
(b) The enhanced mammogram
using CT
Experimental Results Analysis
(a)The original mammogram
(mdb253.bmp)
(b) The enhanced mammogram
using CT
More peaks corresponding to microcalcifications are enhanced
The background has a less magnitude with respect to the peaks:
-The microcalcifications are more visible.
Observation:
Experimental Results Analysis
Experimental Results
(a)Original image (b)CT method (c)The DWT Method
These regions contain :• Clusters of microcalcifications (target)• surrounding tissue (background).
For visualization purposes :The ROI in the original mammogram are marked with a square.
ACHIEVEMENT
Improved Computer Assisted screen of mammogram
Achievements!
Enhancement of MC in digitized mammogramfor diagnostic support system
Figure: Diagnostic support system
MC
Suspected
Digital mammography systems :Presents images to the Radiologist with properly image processing applied.
Achievements!
(b) Enhanced image(mdb238.jpg)
(a) Original imageROI
(mdb238.jpg)
(a) Original imageWHOLE IMAGE (mdb238.jpg)
Digital mammography systems :Presents images to the Radiologist with properly image processing applied.
Hard to find MC Easy to find MC
Whilephysicians
interact with
The information in an image During interpretation process
Achievements!!
Enhancement of MC in digitized mammogram
With improved visual understanding, we can develop :
ways to further improve :o Decision making ando Provide better patient care
Improved Computer Assisted Screening
Goal Accomplished
Another Step Ahead..how about training a machine?
Dealing with Features
Why Feature Extraction?
Finding a feature:That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture colorintensity
Fig: MC features (Extracted Using Human Visual Perception)
Why Feature Extraction?
Finding a feature:That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture colorintensity
Fig: MC (Irregular in shape and size)(Extracted Using Human Visual Perception)
MoreFeatures: Shape Size
Why Feature Extraction?
Problems With MC Features:Irregular in shape and sizeNo definite patternLow Contrast -
Located in dense tissueHardly any color intensity variation
MC Feature
Fig: MC (Irregular in shape and size)(Extracted Using Human Visual Perception)
Why Feature Extraction? MC Feature
How radiologist deals with feature Detection/Recognition issue ?
Using Human Visual Perception
Why Feature Extraction? MC Feature
How Radiologist (Using Human Eye) deals with feature detection/Recognition issue ?
Using Human Visual Perception
Humans are equipped with sense organs e.g. eye-Eye receives sensory inputs and -Transmits sensory information to the brain
http://www.simplypsychology.org/perception-theories.html
Why Feature Extraction? MC Feature
Teach the machine to see like just we doObjective:
Irregular in shape and sizeNo definite patternLow Contrast -
Located in dense tissueHardly any color intensity variation
Machine Vision Challenges:-To make sense of what it sees
In Real:MC is Extracted Using Human Visual Perception
SURF Point Algorithm
Speeded-Up Robust Features (SURF) Algorithm
Point feature algorithm (SURF)Approach:
Improving the prediction performance of CAD Providing a faster, reliable and cost-effective prediction
Features will facilitate:
Fig: MC Point features (Extracted Using SURF point feature algorithm)
Point feature algorithm (SURF)Approach:
SURF point algorithm
Detect a specific object
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Objective
based on Finding point correspondences
between . The reference and the target image
Reference Image Target Image
Context in using the features:
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key pointsII. Matching key pointsIII. Classification
Fig. Putatively Matched Points (Including Outliers )
Context in using the features:
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key pointsII. Matching key pointsIII. Classification
Estimate Geometric Transformation and Eliminate Outliers
Context in using the features:
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key pointsII. Matching key pointsIII. Classification
Moving Towards MC Feature DetectionUsing
SURF Point Algorithm
Local feature
Details In Next slide
To keep in mind
Local Feature Detection and Extraction
Local features :
A pattern or structure :Point, edge, or small image patch.
- A pattern or structure found in an image,
Differs from its immediate surroundings bytexture colorintensity
- Associated with an image patch that:
Fig.1 : Some Image Patch We used for Feature Point Detection Purpose
Local Feature Detection and Extraction
Applications: Image registration Object detection and classification TrackingMotion estimation
Using local features facilitates: handle scale changes rotation occlusion
Detectors /Methods :• FAST• Harris• Shi & Tomasi• MSER
• SURF
Feature Descriptors:SURFFREAKBRISKHOG descriptors
Detecting corner features
detecting blob/point features.
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Detector Feature Type Scale IndependentFAST [24] Corner No
Minimum eigen value algorithm[25]
Corner No
Corner detector [26] Corner NoSURF [27] Blob/ Point YesBRISK [28] Corner YesMSER [29] Region with uniform
intensityYes
Local Feature Detection and Extraction
Why Using SURF Feature?Trying to identify MC cluster Blob
Speeded-Up Robust Features (SURF) algorithm to find blob features.
detectSURFFeatures(boxImage);
selectStrongest(boxPoints, 100)
extractFeatures(boxImage, boxPoints)
matchFeatures(boxFeatures, sceneFeatures);
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Read the reference image containing the object of interest
Read the target image containing a cluttered scene.
Detect feature points in both images.
Select the strongest feature points found in the reference image.
Select the strongest feature points found in the target image.
Extract feature descriptors at the interest points in both images.
Find Putative Point Matches using their descriptors
Display putatively matched features.
Locate the Object in the Scene Using Putative Matches
Start
End
SURF Point Detection
1.Read the reference image
containing MC cluster
2.Target image containing MC.
2.Strongest feature point
in MC cluster
2. Strongest Feature point in Target Image
3. No match point Found
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Are we getting less feature points?
Figure: No match point Found
No. of SURF feature points: 2 No. of SURF feature points: 47
Image Size256*256
Image Size 549*623
Image mdb238.jpg
More features from the image extracted(most points are mismatched)
To extract relevant feature point from the image
Case 1: Consider Big Reference Image
To get more feature points
To extract relevant feature point from the image
Case 2: Consider A bigger Reference Image andWhole mammogram as Target Image
1. Image of MC Cluster(mdb238.jpg) (256*256)
2. Main mammogram (mdb238.jpg) 1024*1024
3. 100 strongest point of ROI) (256*256) 4. 300 strongest point of Main mammogram (mdb238.jpg) 1024*1024
To get more feature points
What we finally have? No putative match Point
To extract relevant feature point from the image
Case 2: Consider A bigger Reference Image andWhole mammogram as Target Image To get more feature points
1. Image of an Microcalcification Cluster
Too small ROI will cause less feature points to match
2. 23 strongest pointsAmong 100 Strongest Feature Points
from reference image
Reference image: mdb248.jpgImage size: 256 *256
detectSURFFeatures(mc_cluster);
Problem 1: less number of feature points to matchSURF Feature Point
4. Only 1 strongest pointsAmong 300 Strongest Feature Points
from Scene Image
Too small ROI will cause less feature points to match
3. Image of a Cluttered Scene
Scene image: mdb248.jpgImage size: 427*588
detectSURFFeatures(sceneImage)
Problem 1: less number of feature points to matchSURF Feature Point
Result of small ROI (256*256):No Putative Point Matches
[mcFeatures, mc_Points] = extractFeatures(mc_cluster, mc_Points);[sceneFeatures, scenePoints] = extractFeatures(sceneImage, scenePoints);mcPairs = matchFeatures(mcFeatures, sceneFeatures);matchedmcPoints = mc_Points(mcPairs(:, 1), :);matchedScenePoints = scenePoints(mcPairs(:, 2), :);showMatchedFeatures(mc_cluster, sceneImage, matchedmcPoints, ... matchedScenePoints, 'montage');
Problem 1: less number of feature points to matchSURF Feature Point
Image Image Size Number of feature points
1190*589 15
588*427 23
256*256 1
541*520 86
Varying image size to see the effect to get SURF feature points
Approach-01 to solve: Considering the Whole image(Label and Pectoral Muscle)
Image size No. of SURF feature points
1024*1024 63
Target:To acquire more feature
2. Irrelevant Feature Points
Image size No. of SURF feature points
1024*1024 63
1. Less Feature points
Approach-01 to solve: Considering the Whole image(Label and Pectoral Muscle)
Target:To acquire more feature
Result:
Image size No. of SURF feature points
255*256 2
Approach-02 : Detect feature from the cropped image
Target:To acquire more feature
Image size No. of SURF feature points
256*256 2
Target:To acquire more feature
2. Relevant Feature Points
1. Less Feature pointsResult:
Approach-02 : Detect feature from the cropped image
Observation from approach 1 and 2
1. Image Size does not affectThe number of Feature Points
2. Zooming an image mayhelp to extract relevant featuresfrom the image(very few points to match)
mdb238.jpgImage Size: 1024*1024
mdb238.jpgImage Size: 256*256
Observation:Varying image size is not helping to get feature points
Image of an Microcalcification Cluster
23 strongest pointsAmong 100 Strongest Feature Points
from reference image
Reference image: mdb248.jpgImage size: 256 *256
Only 1 strongest pointsAmong 300 Strongest Feature Points
from Scene Image
Scene image: mdb248.jpgImage size: 427*588
Observing SURF Drawback
This method works best for :-- Detecting a specific object
(for example, the elephant in the reference image,rather than any elephant.)
-- Non-repeating texture patterns-- Unique feature
This technique is not likely to work well for:-- Uniformly-colored objects-- Objects containing repeating patterns.
detecting blob /point features. AIM Failed
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Image Correlation Technique
Alternate ApproachImage Correlation Technique
Correlation
∑∑ ++=⊗k l
kjkihlkfhf ))((),(=f Image
=h Kernel/Mask
f1 f2 f3
f4 f5 f6
f7 f8 f9
h1 h2 h3
h4 h5 h6
h7 h8 h9
f1h1 f2h2 f3h3
f4h4 f5h5 f6h6
f7h7 f8h8 f9h9
=⊗ hf⊗
Experimental ResultsImage Correlation Technique
Image no: Benign mdb218.jpg
1. Original image
2. Kernel/ Mask/Template
3. Correlation Output
4. Identified MC(High value of sum.)
Image no: Benign mdb219.jpg
Image no: Benign mdb223.jpg
Image no: Benign mdb226.jpg
Image no: Benign mdb227.jpg
Image no: Benign mdb236.jpg
Image no: Benign mdb248.jpg
Image no: Benign mdb252.jpg
(Fixed Template Problem)..
Image no: Benign mdb222.jpg(Fixed Template Problem) Cont….
(Fixed Template Problem)..
Using Gabor Filter
Using Gabor Filter
• Make Gabor patch:
2; 2; 0.7854
2; 0.5; 0.7854
2; 2; 1.5708
5; 0.5; 1.5708
5; 2; 0.7854
2; 0.5; 1.5708
5; 0.5; 0.7854
5; 2; 1.5708
• Correlate the patch with image-To extract features of MC
⊗ =
0 10 20 30 40 50 60 70 80 90 100-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Creating Gabor Mask
1. Linear RAMP
2. Linear RAMP values across: Columns Xm (left) and Rows Ym (Right)
3. Linear RAMP values across - Columns(Xm)
The result in the spatial domain
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5Xm (Across Columns) Ym- (Across rows)
4. Across Columns, Xm :a) Increase frequencyb )Use gray color map
6. Adding Xm and Ymtogether in
different proportions5. Across Rows, Ym :
a) Increase frequencyb )Use gray color map
Creating Gabor Mask
7. Create Gaussian Mask
8. Multiply Grating and Gaussian
Grating Gaussian Mask
Creating Gabor Mask
7. GABOR Mask
Creating Gabor Mask
Alternate ApproachUsing Gabor Filter Gabor kernel
2; 2; 0.7854
2; 0.5; 0.7854
2; 2; 1.5708
5; 0.5; 1.5708
5; 2; 0.7854
2; 0.5; 1.5708
5; 0.5; 0.7854
Scale , frequency, orientation
5; 2; 1.5708
MatrixSize = 26; %always scalar!
Scales = [2, 5];Orientations = [pi/4, pi/2];Frequencies = [0.5, 2];
CenterPoints = [13 13]; %int type (eg. [5 5; 13 13])
CreateMethod = FilterBank.CREATE_CROSSPRODUCT;
010
2030
020
40-0.5
0
0.5
2; 2; 0.7854
010
2030
020
40-0.2
0
0.2
2; 0.5; 0.7854
010
2030
020
40-0.2
0
0.2
2; 0.5; 1.5708
010
203
020
40-0.2
0
0.2
5; 2; 0.7854
010
2030
020
40-0.2
0
0.2
5; 2; 1.5708
010
2030
020
40-0.1
0
0.1
5; 0.5; 0.7854
010
2030
020
40-0.1
0
0.1
5; 0.5; 1.5708
010
2030
020
40-0.5
0
0.5
2; 2; 1.5708
Using Gabor Filter Gabor kernel
; 0 5; 5 08
Using Gabor Filter
⊗
⊗
⊗
=
=
=
Using Gabor Filter
⊗
⊗
⊗
=
=
=
⊗ =
Image In Spatial DomainUsing Gabor Filter Final Scenario
mini-MIAS drawbacksExperimental Realization
mini-MIAS drawbacksBenign mdb218
Original Enhanced
Gabor Effects
mini-MIAS drawbacksBenign mdb218
Original
Enhanced
Gabor Effects
Observation 1
mini-MIAS drawbacksBenign mdb218
Original Enhanced
- NO definite Feature found
Gabor Effects
OBSERVATION-1:
More Evaluation (Gabor)mdb222.jpgBenign
OBSERVATION-1:
-NO definite feature of MC
mini-MIAS drawbacksBenign mdb218
Original Enhanced
Are these really enhanced?
-There is more detail, but could be noise.
Question Arise?
Gabor Effects
mini-MIAS drawbacks
Enhanced version can contain Noise
Experimental Realization
1.Very Poor Quality with .jpg compression effects
a) Original image b) Enhanced image b) Enhanced imagea) Original image
mdb209
mdb213
mdb219
mdb249
mini-MIAS drawbacks
Not good enough for MC to be detectable
Experimental Realization
2. Reduced in resolution
Benign mdb218
Original Enhanced
Observation 2
mini-MIAS drawbacks
Not good enough for MC to be detectable
Experimental Realization
2. Reduced in resolution
Benign mdb218
Original
Enhanced
Where is MC?
OBSERVATION-2:
-There is more detail, but could be noise.
-Enhanced versionseems to contain compression artifacts.
More Evaluation (Gabor)mdb226.jpgBenign
OBSERVATION-2:
- Bad resolution- Noise dominant- No definite feature of MC
More Evaluation (Gabor)mdb227.jpgBenign
OBSERVATION-2:
- Bad resolution/Poor quality image
- No definite feature of MC
More Evaluation (Gabor)mdb236.jpgBenign
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)mdb240.jpgBenign
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)mdb209.jpgMalignant
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)mdb211.jpgMalignant
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)mdb213.jpgMalignant
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb231.jpg
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb238.jpg
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb253.jpg
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb256.jpg
OBSERVATION-2:
- Bad resolution-No definite feature of MC- Noise dominant
Observation 3
More Evaluation (Gabor)mdb219.jpgBenign
OBSERVATION-3:
-Image Smoothing to remove edge will
Vanish the existenceof MC
-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb239.jpg
OBSERVATION-3:
-Image Smoothing to remove edge will
Vanish the existenceof MC
-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb241.jpg
OBSERVATION-3:
-Image Smoothing to remove edge will
Vanish the existenceof MC
-No definite feature of MC- Noise dominant
More Evaluation (Gabor)Malignant mdb249.jpg
OBSERVATION-3:
-Image Smoothing to remove edge will
Vanish the existenceof MC
-No definite feature of MC- Noise dominant
Observation 4,5,6
More Evaluation (Gabor)mdb223.jpgBenign
OBSERVATION-4:
-NO definite feature of MCFalse contour
More Evaluation (Gabor)mdb223.jpgBenign
OBSERVATION-5:
-NO definite feature of MCFalse contour
No feature
More Evaluation (Gabor)mdb223.jpgBenign
OBSERVATION-6:
-NO definite feature of MCFalse contour
No feature
Several similar area false positive o/p
Observation 7
More Evaluation (Gabor)mdb248.jpgBenign
OBSERVATION-7:
-feature of MC-But MC has different
orientationin different image
More Evaluation (Gabor)mdb252.jpgBenign
OBSERVATION-7:
-feature of MC-But MC has different
orientationin different image
Observation &
Drawing Conclusion
Future detection
Observation & Drawing Conclusion Feature Detection
• Reduced in resolution(Is not good enough for MC to be detectable)
• Very Poor Quality with .jpeg compression effects(Original MIAS doesn’t have such artifacts)
Limitations of mini-MIAS:
What can be done using mini-MIAS ?
• Can be used for big object detection(Pectoral Muscle, X-ray Label, Tumor, Mass detection)
Conclusion: mini-MIAS is not a good choice for:MC feature extraction
Observation & Drawing Conclusion Feature Detection
Any alternative to mini-MIAS?
Observation & Drawing Conclusion Feature Detection
Database Name Authority
MIAS ( Mammographic Image Analysis Society Digital Mammogram Database)
Mammography Image Analysis Society- an
organization of UK research groups
DDSM (Digital Database for Screening Mammogram) University Of South Florida, USA
NDM (National Mammography Database) American College Of Radiology, USA
LLNL/UCSF Database
Lawrence Livermore National Laboratories
(LLNL), University of California at San Fransisco (UCSF)
Radiology Dept.
Observation & Drawing Conclusion Feature Detection
Database Name Authority
Washington University Digital Mammography Database Department of Radiology at the
University of Washington, USA
Nijmegen Database Department of Radiology at the
University of Nijmegen, the
Netherlands
Málaga mammographic database University of Malaga Central Research
Service (SCAI) ,Spain
BancoWeb LAPIMO Database Electrical Engineering Department at
Universidad de São Paulo, Brazil
Observation & Drawing Conclusion Feature Detection
These databases are NOT FREE
Research Findings
5; 0.5; 0.7854
Research FindingsImproved computer assisted
screening of mammogram
Detection and removal of big objects:- Pectoral Muscle - X-ray level
MC
Suspected
Observation & Drawing Conclusion On
Feature Detection
• Reduced in resolution(Is not good enough for MC to be detectable)
• Very Poor Quality with .jpeg compression effects(Original MIAS doesn’t have such artifacts)
Limitations of mini-MIAS:
What can be done using mini-MIAS ?
• Can be used for big object detection(Pectoral Muscle, X-ray Label, Tumor, Mass detection)
Conclusion: mini-MIAS is not a good choice for:MC feature extraction
BesideResearch Findings…
Published PaperAvailable Online:
http://cennser.org/IJCVSP/paper.html
Published PaperAvailable Online:
http://cennser.org/IJCVSP/paper.html
Published PaperAvailable Online:
http://cennser.org/IJCVSP/paper.html
Submitted Paperhttp://www.journals.elsevier.com/image-and-vision-computing/
Further Research ScopeThere is always more to work on..In Research:
Future Plan
1. Segment the image
2. Find out the feature from the segmented image
3. Train the machine with features:
-ANN (Artificial Neural Network)-SVM (Support Vector Machine)
- GentleBoost Classifier [30]
4. Identify the MC5. Classify the MC
Available options
[1]Heinlein P., Drexl J. and Schneider Wilfried: Integrated Wavelets for Enhancement ofMicrocalcifications in Digital Mammography, IEEE Transactions on Medical Imaging, Vol.22, (2003) pp. 402-413
[2]. Zhibo Lu, Tianzi Jiang, Guoen Hu, Xin Wang: Contourlet based mammographicimage enhancement, Proc. of SPIE, vol. 6534, (2007) pp. 65340M-1 - 65340M-8
[3]A.Papadopoulos, D.I . Fotiadis, L.Costrrido,” Improvement of microcalcification clusterdetection in mammogaphy utilizing image enhancement techniques”.Comput.Bio.Med.10,Vol 38,Issue 38,pp.1045-1055,2008
[4]M.Rizzi, M.D’Aloia, B.Castagnolo,” Computer aided detection of microcalcification in digitalMammograms adopting a wavelet decomposition ”,Integr.Comput.-Aided Eng.,Vol 16,Issue 2,pp.91-103,2009
[5]D.Narain Ponraj, M.Evangelin Jenifer, P. Poongodi, J.Samuel Manoharan “A Survey on thePreprocessing Techniques of Mammogram for the Detection of Breast Cancer”, Journal ofEmerging Trends in Computing and Information Sciences, Volume 2, Issue 12, pp. 656-664,2011
Reference
[6]K. Santle Camilus , V. K. Govindan, P.S. Sathidevi,” Pectoral muscle identification inmammograms”, Journal of Applied Clinical Medical Physics , Vol. 12 , Issue No. 3 , 2011
[7] Leeuw H.D., Stehouwer BL, Bakker CJ, Klomp DW, Diest PV, Luijten PR, Seevinck PR,Bosch MA, Viergever MA, Veldhuis WB:Detecting breast microcalcifications with high-field MRI, NMR in Biomedicine,Vol 27, Issue 5, pages 539–546,2014
[8]N.R.Pal,B.Bhowmik, S.K.Patel, S.Pal, J.Das,”A multi-stage nural network aided system fordetection of microcalcification in digitized mammogeams”,Neurocomputing, Vol 11,pp.2625-2634,2008
[9]S.N.Yu, Y.K. Huang,” Detection of microcalcifications on digital mammograms using combined Model-based and statistical textural features”, Expert Syst.Appl. , Vol 37,Issue 7,pp.5461-5469, 2010
[10]Arnau Oliver, Albert Torrent, Meritxell Tortajada, Xavier Llad´o,Marta Peracaula,Lidia Tortajada, Melcior Sent´ıs, and Jordi Freixenet,” Automatic microcalcification andcluster detection for digital and digitised mammograms”, Springer-Verlag BerlinHeidelberg, 36, pp. 251–258, 2010
Reference
[11] Balakumaran T., Vennila ILA, Shankar C.G: Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering, International Journal of Computer Science and Information Security, Vol 7,Issue 1,pp.121-125,2010
[12] Arnau Olivera, Albert Torrenta , Xavier Lladóa , Meritxell Tortajada, LidiaTortajadab, Melcior Sentísb, Jordi Freixeneta, Reyer Zwiggelaarc,” Automaticmicrocalcification and cluster detection for digital and digitisedmammograms”, Elsevier:Knowledge-Based Systems, Volume 28, pp. 68–75, April 2012.
[13] Zhang X., Homma N., Goto S.,Kawasumi Y., Ihibashi T.,Abe M.,Sugita N.,Yoshizawa M:A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms, Journal of Medical Engineering, Vol 3,Issue 1,pp.111-119,2013
[14]Wang T. C and Karayiannis N. B.: Detection of Microcalci¯cations in Digital Mammograms Using Wavelets, IEEE Transaction on Medical Imaging, vol. 17, no. 4,(1989) pp. 498-509
[15]. Daubechies I.: Ten Lectures on Wavelets, Philadelphia, PA, SIAM, (1992)
Reference
Reference
[16] Strickland R.N. and Hahn H.: Wavelet transforms for detecting microcalcificationsin mammograms, IEEE Transactions on Medical Imaging, vol. 15, (1996) pp. 218-229
[17] Lu J., Ikehara T., Zhang Y,Mihara T., Itoh T.,Maeda R:High quality factor silicon cantilever driven by piezoelectric thin film actuator for resonant based mass detection, Micro system Technologies , Vol 15, Issue 8, pp:1163-1169., 2009
[18]Fatemeh Moayedi, Zohreh Azimifar, Reza Boostani, and Serajodin Katebi: Contourlet-based mammography mass classification, ICIAR 2007, LNCS 4633,(2007) pp. 923-934
[19] Shankla V, David D. P, Susan P. Weinstein; Michael D., Tuite C, Roth R., Emily F:Automatic insertion of simulated microcalcification clusters in a software breast phantom, , Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 2014
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
Reference[21] Da Cunha A. L., Zhou J. and Do M. N,: The Nonsubsampled Contourlet Transform:Theory, Design, and Applications, IEEE Transactions on Image Processing,vol. 15, (2006)pp. 3089-3101
[22]Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement bymultiscale analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994)pp. 7250-7260
[23] Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques forMammographic Breast Masses, IEEE Transactions on Information Technology inBiomedicine, vol. 9, (2005) pp. 109-119
[24] Rosten, E., and T. Drummond. "Machine Learning for High-Speed Corner Detection." 9th European Conference on Computer Vision. Vol. 1, 2006, pp. 430–443.
[25] Shi, J., and C. Tomasi. "Good Features to Track." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. June 1994, pp. 593–600.
[26] Harris, C., and M. J. Stephens. "A Combined Corner and Edge Detector." Proceedings ofthe 4th Alvey Vision Conference. August 1988, pp. 147–152.
[27] Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. "SURF: Speeded Up RobustFeatures." Computer Vision and Image Understanding (CVIU). Vol. 110, No. 3, 2008, pp.346–359.
[28] Leutenegger, S., M. Chli, and R. Siegwart. "BRISK: Binary Robust Invariant Scalable
[29] Matas, J., O. Chum, M. Urba, and T. Pajdla. "Robust wide-baseline stereo frommaximally stable extremal regions."Proceedings of British Machine Vision Conference.2002, pp. 384–396.
[30] Oliver A.; Torrent A. , Tortajada M, Liado X, R., Preacaula M , Tortajada L., Srntis M.,Ferixenet J: A Boosting based approach for automatic Microcalcification Detection,Springer-Verlag Berlin Heldelberg,Lecture notes on Computer Science (LNCS 6136), (2010)pp. 251- 258
Reference
Thank you foryour time and attention