Ms thesis-final-defense-presentation

Computer Assisted Screening of
Microcalcifications in Digitized
Mammogram for Early
Detection of Breast Cancer
Thesis Presentation
Nashid Alam
Registration No: 2012321028
annanya_cse@yahoo.co.uk
Supervisor: Prof. Dr. Mohammed Jahirul Islam
Department of Computer Science and Engineering
Shahjalal University of Science and Technology
Driving research for better breast cancer treatment
“The best protection is early detection”
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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
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
Mammography Machine

Mammography
USE:
I. Viewing x-ray image
II. Manipulate X-ray image on a computer screen
Mammography :
 Process of using low-energy
x-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
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 and
clinically acceptable results.
Drawbacks:
Literature Review
local range modification algorithm
Integrated wavelets form MC model
Stein's thresholding [7]for denoising
Use Contourlets
Method used:
Watershed transformation
Boundary based method
Hybrid techniques
Thresholding 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 MC
Weighted density function
Fuzzy Shell Clustering
Training stage: Pixel-based boosting classifier
Multi-layered perception network
Back 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 filtering
discrete wavelet linear stretching and shrinkage algorithm.
low-frequency subbands are discarded
biorthogonal 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 masses
instead of microcalcifications
Zhibo et.al.(2007)[2]
Lu et.al.(2013) [17]
Goals:
Drawbacks:
Method used:
Mass Detection
Multiscale regularized reconstruction
Hybrid Image Filtering Method
Noise regularization in DBT reconstruction
Use Contourlets
- Detecting subtle mass lesions
in Digital breast tomosynthesis (DBT)
- Only detect large mass
Digital Breast
Tomosynthesis captures
PHOTO COURTESY :
http://www.itnonline.com/article/trends-breast-imaging
http://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 and
clinically 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 of
a virtual clinical trial (VCT) :
-Simulation of breast anatomy,
- Mechanical compression
- Image acquisition
- Image processing
- Image displaying and interpretation.
Shankla et.al.(2014)[19]

Burdensome Task Of Radiologist :
Eye fatigue:
-Huge volume of images
-Detection accuracy rate tends to decrease
Non-systematic search patterns of humans
Performance gap between :
Specialized breast imagers and
general 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
Calcifications
Tumor
Lesion
Lump
Individual Research Areas
Problem Statement

Motivation to the research: Goal
Better Cancer Survival Rates
(Facilitate Early Detection ).
Provide “second opinion” : Computerized decision
support systems
Fast,
Reliable, and
Cost-effective
Overcome:
The development of breast cancer

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 database
http://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
7
Normal
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 Semester
M-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 Semester
M-3

Schematic representation of the system

Schematicrepresentation
ofthesystem

Removing Pectoral Muscle
And
X-ray Label

X-ray Label Removing Finding The Big BLOB
The types X-ray Label:
High Intensity Rectangular Label
Low Intensity Label
Tape 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.Histogram
Equalization
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 method
bw_image = im2bw(contrast_image, level);%getting binary image

5.Finding biggest blob
6.Hole filling
Inside 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

Experimental results

1.Original image
2.Histogram
Equalization 3.Contrast Image 4.Binary Image 5.Finding biggest blob
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb212.jpg
mdb214.jpg
mdb214.jpg
mdb218.jpg
mdb219.jpg
Benign

X-ray Label Removing Benign
1.Original image
2.Histogram
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb222.jpg
mdb223.jpg
mdb226jpg
mdb227jpg

X-ray Label Removing Benign
1.Original image
2.Histogram
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb226.jpg
mdb240.jpg
mdb248.jpg
mdb252.jpg

X-ray Label Removing Malignant
1.Original image
2.Histogram
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb209.jpg
mdb211.jpg
mdb213.jpg
mdb216.jpg
mdb231.jpg

1.Original image
2.Histogram
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb233.jpg
mdb238.jpg
mdb239.jpg
mdb241.jpg

1.Original image
2.Histogram
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
mdb245.jpg
mdb249.jpg
mdb253.jpg
mdb256.jpg

Successful
Finally!

Removing pectoral muscle
Keeping 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 Fatty tissue area
o Duct
o Lobules
o Sinus
o ligaments
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 muscle
Points to be noted :
-Pectoral muscle a Triangular area
mdb212.jpg
mdb214.jpg
Based on this point:
Moving on towards solution
mdb209.jpg
(2)Binary Image(1)Original Image

Triangle Detection
of pectoral muscle
1. Find the triangular area of the pectoral muscle region
I. Finding white seeding point
II. Finding the 1st black point of 1st row after getting a white seeding point
III. Draw a horizontal line in these two points.
IV. finding the 1st black point of 1st column after getting a white seeding point
V. 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 muscle
Approach-03(Triangle Detection of pectoral muscle):
mdb212.jpg
1.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 muscle
4.Triangle
5.Triangle Filled
6.muscle removed

Experimental results
Triangle Detection
of pectoral muscle

Triangle Detection
of 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 muscle
mdb222.jpg
mdb226.jpg
mdb227.jpg
2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle
Problems faced
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
Triangle Detection
of 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 muscle
Problems faced:
Defects in mammogram (Horizontal Stripe Missing)
Solution: Replicate the 2nd and 3rd row)
Class: Benign

Triangle Detection
of 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

Successful
Pectoral Muscle Removing
Finally!

Improved
Computer Assisted Screening
Enhancement of digitized mammogram
Goal

MAIN NOVELTY
Input image
Bandpass
Directional
subbands
Bandpass
Directional
subbands

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
Bandpass
Directional
subbands
Bandpass
Directional
subbands

Why Contourlet?
•Decompose the mammographic image:
-Into directional components:
To easily capture the geometry of the image features.
Target

Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
Frequency 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

Decomposition
Laplacian Pyramid Level-1
8 Direction
4 Direction
4 Direction
(mdb252.jpg)

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

Decomposition
Contourlet coefficient at level 4
Wedge-shape frequency band is 23=8.
sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7

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

Directional 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

Directional filter banks
sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7

Input image
Bandpass
Directional
subbands
Bandpass
Directional
subbands
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 CT is implemented by:
Laplacian pyramid followed by directional filter banks (Fig-01)
Input image
Bandpass
Directional
subbands
Bandpass
Directional
subbands
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
Bandpass
Directional
subbands
Bandpass
Directional
subbands
Details….
(c)
Denote
Each subband by yi,j
Where
i =decomposition level and
J=direction
Decomposes 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.
Denote
Where
J=direction Details….

Details….
The directional subbands are enhanced using equation 2.
=)( , jiyf
)2,1(,1 nnW jiy
)2,1(,2 nnW jiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1
………..(2)
Denote
Where
J=direction
W1= weight factors for detecting the surrounding tissue
W2= 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

The directional subbands are enhanced using equation 2.
=)( , jiyf
)2,1(,1 nnW jiy
)2,1(,2 nnW jiy
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
Details….
The microcalcifications
appear :
On each subband
Over a very
homogeneous background.
Most of the transform coefficients:
-The coefficients corresponding to the
injuries 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
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.

Applying Contourlet Transformation Benign
Original image Enhanced image
Goal: Microcalcification Enhancement
mdb222.jpg
mdb223.jpg
mdb248.jpg
mdb252.jpg

mdb226.jpg
mdb227.jpg
mdb236.jpg
mdb240.jpg

Original image Enhanced image Original image Enhanced image
mdb218.jpgmdb219.jpg

Applying Contourlet Transformation Malignant
mdb209.jpg
mdb211.jpg
mdb213.jpg
mdb231.jpg

mdb238.jpg
mdb239.jpg
mdb241.jpg
mdb249.jpg

mdb253.jpg
mdb256.jpg

Applying Contourlet Transformation Normal
mdb003.jpg
mdb004.jpg
mdb006.jpg
mdb007.jpg

mdb009.jpg
mdb018.jpg
mdb027.jpg
mdb033.jpg

mdb046.jpg
mdb056.jpg
mdb060.jpg
mdb066.jpg

mdb070.jpg
mdb073.jpg
mdb074.jpg
mdb076.jpg

mdb093.jpg
mdb096.jpg
mdb101.jpg
mdb012.jpg

mdb128.jpg
mdb137.jpg
mdb146.jpg
mdb154.jpg

mdb166.jpg
mdb169.jpg
mdb224.jpg
mdb225.jpg

mdb263.jpg
mdb294.jpg
mdb316.jpg
mdb320.jpg

Use Separable Transform
2D Wavelet Transform
Visualization
Label of
approximation
Horizontal
Details
Horizontal
Details
Vertical
Details
Diagonal
Details
Vertical
Details
Diagonal
Details

Decomposition at
Label 4
Original image
(with diagonal details areas indicated)
Diagonal Details

Vertical Details
Decomposition at
Label 4
Original image
(with Vertical details areas indicated)

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

DWT
1.Original Image
(Malignent_mdb238) 2.Decomposition at Label 4

1.Original Image
(Benign_mdb252)
2.Decomposition at Label 4
DWT

1.Original Image
(Malignent_mdb253.jpg) 2.Decomposition at Label 4

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) and
3. 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, IEEE
Transactions on Information Technology in Biomedicine, vol. 9, (2005) pp. 109-119

Metrics
1. Measurement of Distributed Separation
(MDS)
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 image
µucalc0 = Mean of the microcalcification region of the original image
µtissueE = Mean of the surrounding tissue of the enhanced image
µtissue0 = Mean of the surrounding tissue of the enhanced image
Defined by:
Where:

Metrics
2. Contrast enhancement of background against
target (CEBT)
The CEBT Quantifies :
The improvement in difference between the background and the target(MC).
…………………………(4)
0µucalc
Eµucalc
0µtissue
0µucalc
Eµtissue
Eµucalc
CEBT
σ
σ
−
=
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)
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)
0µucalc
Eµucalc
0µtissue
0µucalc
Eµtissue
Eµucalc
ECEBT
ε
ζ
−
=
= Entropy of the microcalcifications region in the enhanced image
= Entropy of the microcalcifications region in the original image
Eµucalcζ
0µucalcε

MDS, CEBT and ECEBT metrics on the enhanced mammograms
CT Method DWT Method
MDS CEBT ECEBT MDS CEBT ECEBT
0.853 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.
Experimental Results Analysis

0
0.2
0.4
0.6
0.8
1
1.2
TBCE
Mammogram
CEBT Matrix
CT DWT

0
0.2
0.4
0.6
0.8
1
1.2
DSM
Mammogram
ECEBT Matrix
CT DWT

Mesh plot of a ROI containing microcalcifications
(a)The original
mammogram
(mdb252.bmp)
(b) The enhanced
mammogram
using CT

(a)The original
mammogram
(mdb238.bmp)
(b) The enhanced
mammogram
using CT

(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:

(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 mammogram
for 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 image
ROI
(mdb238.jpg)
(a) Original image
WHOLE IMAGE
(mdb238.jpg)
Digital mammography systems :
Presents images to the Radiologist
with properly image processing applied.
Hard to find MC Easy to find MC
While
physicians
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 and
o Provide better patient care
Improved
Computer Assisted Screening
Goal Accomplished

Another Step Ahead..how about training a machine?

Why Feature Extraction?
Finding a feature:
That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture
 color
intensity
Fig: MC features (Extracted Using Human Visual Perception)

Finding a feature:
That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture
 color
intensity
Fig: MC (Irregular in shape and size)
(Extracted Using Human Visual Perception)
More
Features:
 Shape
 Size

Problems With MC Features:
Irregular in shape and size
No definite pattern
Low Contrast -
Located in dense tissue
Hardly 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

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

Teach the machine to see like just we doObjective:
Irregular in shape and size
No definite pattern
Low Contrast -
Located in dense tissue
Hardly 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 algorithm
I. Finding Key points
II. Matching key points
III. Classification
Fig. Putatively Matched Points (Including Outliers )

III. Classification
Estimate Geometric Transformation and Eliminate Outliers

III. Classification

Moving Towards MC Feature Detection
Using
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 by
texture
 color
intensity
- Associated with an image patch that:
Fig.1 : Some Image Patch We used for Feature Point Detection Purpose

Applications:
 Image registration
 Object detection and classification
 Tracking
 Motion estimation
Using local features
facilitates:
 handle scale changes
 rotation
 occlusion
Detectors /Methods :
• FAST
• Harris
• Shi & Tomasi
• MSER
• SURF
Feature Descriptors:
SURF
FREAK
BRISK
HOG descriptors
Detecting corner features
detecting blob/point features.

Detector Feature Type Scale Independent
FAST [24] Corner No
Minimum eigen value
algorithm[25]
Corner No
Corner detector [26] Corner No
SURF [27] Blob/ Point Yes
BRISK [28] Corner Yes
MSER [29] Region with uniform
intensity
Yes
Why Using SURF Feature?
Trying to identify MC cluster Blob

detectSURFFeatures(boxImage);
selectStrongest(boxPoints,100)
extractFeatures(boxImage,boxPoints)
matchFeatures(boxFeatures,sceneFeatures);
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

Are we getting less feature points?
Figure: No match point Found

No. of SURF feature points: 2 No. of SURF feature points: 47
Image Size
256*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

Case 2: Consider A bigger Reference Image and
Whole 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

What we finally have? No putative match Point
Case 2: Consider A bigger Reference Image and
Whole mammogram as Target Image

1. Image of an Microcalcification Cluster
Too small ROI will cause less feature points to match
2. 23 strongest points
Among 100 Strongest Feature Points
from reference image
Reference image: mdb248.jpg
Image size: 256 *256
detectSURFFeatures(mc_cluster);
Problem 1: less number of feature points to match
SURF Feature Point

4. Only 1 strongest points
from Scene Image
Too small ROI will cause less feature points to match
3. Image of a Cluttered Scene
Scene image: mdb248.jpg
Image size: 427*588
detectSURFFeatures(sceneImage)
SURF 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');
SURF 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
feature points
1024*1024 63
1. Less Feature points
Approach-01 to solve:
Considering the Whole image(Label and Pectoral Muscle)
Target:
Result:

feature points
255*256 2
Approach-02 :
Detect feature from the cropped image
Target:

feature points
256*256 2
Target:
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 affect
The number of Feature Points
2. Zooming an image may
help to extract relevant features
from the image
(very few points to match)
mdb238.jpg
Image Size: 1024*1024
mdb238.jpg
Image Size: 256*256

Observation:
Varying image size is not helping to get feature points
Image of an Microcalcification Cluster
23 strongest points
from reference image
Reference image: mdb248.jpg
Image size: 256 *256
Only 1 strongest points
from Scene Image
Scene image: mdb248.jpg
Image 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

Alternate Approach
Image 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
⊗

Image 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 mdb222.jpg
(Fixed Template Problem) Cont….

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
0.5
Xm (Across Columns)
Ym- (Across rows)

4. Across Columns, Xm :
a) Increase frequency
b )Use gray color map
6. Adding Xm and Ym
together in
different proportions
5. Across Rows, Ym :
a) Increase frequency
b )Use gray color map
Creating Gabor Mask

7. Create Gaussian Mask
8. Multiply Grating and Gaussian
GratingGaussian Mask
Creating Gabor Mask

7. GABOR Mask
Creating Gabor Mask

Alternate Approach
Using Gabor Filter Gabor kernel
2; 2; 0.78542; 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;

0
10
20
30
0
20
40
-0.5
0
0.5
2; 2; 0.7854
0
10
20
30
0
20
40
-0.2
0
0.2
2; 0.5; 0.7854
0
10
20
30
0
20
40
-0.2
0
0.2
2; 0.5; 1.5708
0
10
20
3
0
20
40
-0.2
0
0.2
5; 2; 0.7854
0
10
20
30
0
20
40
-0.2
0
0.2
5; 2; 1.5708
0
10
20
30
0
20
40
-0.1
0
0.1
5; 0.5; 0.7854
0
10
20
30
0
20
40
-0.1
0
0.1
5; 0.5; 1.5708
0
10
20
30
0
20
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 drawbacks
Experimental Realization

mini-MIAS drawbacks
Benign mdb218
Original Enhanced
Gabor Effects

mini-MIAS drawbacks
Benign mdb218
Original
Enhanced
Gabor Effects

mini-MIAS drawbacks
Benign 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 drawbacks
Benign 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
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
2. Reduced in resolution
Benign mdb218
Original Enhanced

mini-MIAS drawbacks
Not good enough for MC to be detectable
2. Reduced in resolution
Benign mdb218
Original
Enhanced
Where is MC?
OBSERVATION-2:
-There is more detail,
but could be noise.
-Enhanced version
seems to contain
compression artifacts.

mdb226.jpgBenign
OBSERVATION-2:
- Bad resolution
- Noise dominant
- No definite feature of MC

mdb227.jpgBenign
OBSERVATION-2:
- Bad resolution/Poor
quality image
- No definite feature of MC

mdb236.jpgBenign
OBSERVATION-2:
- Bad resolution
-No definite feature of MC
- Noise dominant

mdb240.jpgBenign
OBSERVATION-2:
- Bad resolution
- Noise dominant

mdb209.jpgMalignant
OBSERVATION-2:
- Bad resolution
- Noise dominant

mdb211.jpgMalignant
OBSERVATION-2:
- Bad resolution
- Noise dominant

mdb213.jpgMalignant
OBSERVATION-2:
- Bad resolution
- Noise dominant

Malignant mdb231.jpg
OBSERVATION-2:
- Bad resolution
- Noise dominant

OBSERVATION-2:
- Bad resolution
- Noise dominant

mdb219.jpgBenign
OBSERVATION-3:
-Image Smoothing
to remove edge will
Vanish the existence
of MC
- Noise dominant

OBSERVATION-3:
-Image Smoothing
to remove edge will
of MC
- Noise dominant

mdb223.jpgBenign
OBSERVATION-4:
False contour

mdb223.jpgBenign
OBSERVATION-5:
False contour
No feature

mdb223.jpgBenign
OBSERVATION-6:
False contour
No feature
Several similar area false positive o/p

mdb248.jpgBenign
OBSERVATION-7:
-feature of MC
-But MC has different
orientation
in different image

mdb252.jpgBenign
OBSERVATION-7:
-feature of MC
-But MC has different
orientation
in different image

Observation
&
Drawing Conclusion
Future detection

Observation & Drawing Conclusion Feature Detection
• Very Poor Quality with .jpeg compression effects
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

Any alternative to mini-MIAS?

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.

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

These databases are NOT FREE

Research Findings
5; 0.5; 0.7854

Research Findings
Improved computer assisted
screening of mammogram
Detection and removal of big objects:
- Pectoral Muscle
- X-ray level
MC
Suspected

Observation & Drawing Conclusion On
Feature Detection
• Very Poor Quality with .jpeg compression effects
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
Beside
Research Findings…

Published Paper
Available Online:
http://cennser.org/IJCVSP/paper.html

Submitted Paper
http://www.journals.elsevier.com/image-and-vision-computing/

Further Research Scope
There 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 MC
5. Classify the MC
Available
options

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