Microcalcification Enhancement in Digital Mammogram
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Transcript of Microcalcification Enhancement in Digital Mammogram
Microcalcification Enhancementin digital mammogramMasters -2 Work-progress Presentation #1
Nashid AlamRegistration No: [email protected]
Supervisor: Prof. Dr. M. Shahidur Rahman
Department of Computer Science And Engineering
Shahjalal University of Science and TechnologyWednesday, September 3, 2014
“The best protection is early detection”
Introduction
Breast cancer:The most devastating and deadly diseases for women.
o Computer aided detection (CADe) o Computer aided diagnosis (CADx) systems
Computerize Breast cancer Detection System:
Steps to control breast cancer:1) Prevention2) Detection3) Diagnosis4) Treatment
We will emphasis on :1) Detection2) Diagnosis
Micro-calcification
Mammography
Mammogram
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 [4].)
3. considered regions of high frequency.
They are caused by a number of reasons:
Aging - The majority of diagnoses are made in women over 50
Genetic - Involving the BRCA1 (breast cancer 1, early onset) andBRCA2 (breast cancer 2, early onset) genes
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
Mammogram:A mammogram is an x-ray picture of the breast
Use:To look for changes that are not normal.
Result Archive:The results are recorded on x-ray film or directly into a computer
Types of mammograms:
I. Screening mammograms-Done for women who have no symptoms of breast cancer.
II. Diagnostic mammograms -To check for breast cancer after a lump or other symptom or sign of breast cancer has been found.
III. Digital mammogram-Uses x-rays to produce an image of the breast. The image is stored directly on a computer.
mdb226.jpg
Problem Statement
Main challenge :QUICKLY AND ACCURATELY overcome the development of breast cancer
Problem Statement
Reason behind the problem:Burdensome Task Of Radiologist :
Eye fatigueHuge volume of images
Detection accuracy rate tends to decreaseNon-systematic search patterns of humansPerformance gap between :
Specialized breast imagers andgeneral radiologists
Interpretational Errors:Similar characteristics:
Abnormal and normal microcalcification
Problem Statement
The signs of breast cancer are: Masses CalcificationsTumorLesionLump
Individual Research Areas
A key area of research activity involves :Developing better ways-
To diagnose and stage breast cancer.
• Develop a logistic model:
• Early detection of Breast Cancer.
-Micro-calcification detection
GOAL
-To determine the likelihood of CANCEROUS AREA
from the image values of mammograms.
The Micro-calcification: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.
Why our work is important?
-Better Cancer Survival Rates(Early Detection ).
-The diagnostic management of breast cancer (a difficulttask)
--Radiologist fails to detect Breast Cancer.
-Computerized decision support systems provide“second opinion” :
Fast,Reliable, andCost-effective
Strickland et.at (1996) :A biorthogonal filter bank is used
-To compute four dyadic and -Two cinterpolation scales.
A binary threshold-operator is applied to the six scales.
Literature Review
Laine et.al (1994) :A hexagonal wavelet transform (HWT) is used:
-To obtain multi-scales edges at orientations of 60, 0 and -60 degrees.
The resulting subbands are enhanced and The image reconstructed.
Literature Review
Wang et.al.(1989):The mammograms are:
-Decomposed into different frequency subbands.
The low-frequency subband discarded.
The image is reconstructed from the subbands containing only high frequencies.
Literature Review
Heinlein et.al(2003):For general enhancement of mammograms:
From a model of microcalcifications -The integrated wavelets are derived
Literature Review
Zhibo et.al.(2007):A method aimed at minimizing image noise.
Optimize contrast of mammographic image featuresEmphasize mammographic features:
A nonlinear mapping function is applied:-To the set of coefficient from each level.
Use Contourlets:For more accurate detection of microcalcification clusters
The transformed image is denoised-using stein's thresholding [18].
The results presented correspond to the enhancement of regions with large masses only.
Literature Review
Fatemeh et.al.(2007) :
Focus on:
-Analysis of large masses instead of microcalcifications.
- Detect /Classify mammograms:
Normal and Abnormal
Use Contourlets Transform:
For automatic mass classification
Literature Review
Literature Review
Daubechies I.(1992): Wavelets are mainly used :
-Because of their dilation and translation properties-Suitable for non stationary signals.
Main Novelty
- Nonsubsampled Contourlet Transform
- Specific Edge Filter :To enhance the directional structures of the image in
the contourlet domain.
- Recover an approximation of the mammogram (with the microcalcifications enhanced):
Inverse contourlet transform is applied
Details in upcoming slides
Achievement
The proposed method
Outperforms
The current method
Contourlet transformation(CT)
based on:
Discrete wavelet transform(DWT)
based on:
Details in upcoming slides
Contourlet transformation
Implementation Based On :
• A Laplacian Pyramid decomposition followed by -
• directional filter banks applied on each band pass sub-band.
The non-subsampled contourlet transform extracts:• the geometric information of images.•which can be used to distinguish noises from weak edges.
Details in upcoming slides
Why Contourlet?
•Decompose the mammographic image into well localized and
directional components:
To easily capture the geometry of the image features.
•Accomplished by the 2-D Contourlet Transform (2D-CT) :
Improves the representation scarcity of images over the Discrete DWT [11], [12],[13], [14].
Target:
Details in upcoming slides
• This decomposition offers:-Multiscale and time frequency localization and -A high degree of directionality and anisotropy.
Usefulness of Conterlet :
Why Contourlet?
2-D Contourlet Transform (2D-CT) Discrete DWT
Handles singularities such as edges in a more powerful way
Has basis functions at many orientations has basis functions at three orientations
Basis functions appear a several aspectratios
the aspect ratio of WT is 1
CT similar as DWT can beimplemented using iterative filter banks.
Advantage of using 2D-CT over DWT:
Details in upcoming slides
Wavelet
Good temper resolution in high frequencies
Good frequency resolution in low pass band
OBTAION:
Wavelet
A high pass filter
Temper resolution : A vertical high-resolutionFrequency resolution : The sample frequency divided by the number of samples
O/P of Low Pass Filter High Pass Filter = A Band Pass Result
Wavelet
Working with wavelet:1. Convolve the signal with wavelet filter(h/g)2. Store the results in coefficients/frequency response
(Result in number is put in the boxes)3. Coefficients/frequency response:
- The representation of the signal in the new domain.
Properties:• Maximum frequency depends on the length of the signal.• Recursive partitioning of the lowest band in subjective to the application.
Details in upcoming slides
1.A length 8 signal
3.Convolve the signal with the high pass filter
2.Split/divide the signal in two parts
Wavelet
To avoid redundancy
Down sample by 2
Wavelet
• For perfect low pass filter• Leave everything intact in 0 (zero)
Spectrodensity of the signal at this point
Unit cell
Unit cell is shrunk by half(1/2)
Wavelet
No information loss due to shrinking
First partitioning of lower and higher frequency band
Wavelet
Spectrodensity of the signal at this point
For perfect low pass filter For perfect high pass filter
This works even not for perfect high pass/low pass filter
Wavelet
Split the signalAnd
down-sample by 2In high frequency
Details at level 1
Wavelet
Split inthe low frequency
Details at level 2
Wavelet
Extra Split inthe low frequency
Details at level 3
Wavelet
Approximationat level 3
Approximationat level 2
Approximationat level 1
Wavelet
Works for Signals of 8 samples
23= 8, Sample=8, level=3.
Wavelet
Positive half of the
frequency axis
1
1 2 3 4
Wavelet
Positive half of the
frequency axis
2
1 21
1 2 3 4
Wavelet
Positive half
of
the frequency axis
31
2
1 21
1 2 3 4
Wavelet
Positive half
of
the frequency axis
Details at level 2
Details at level 3
Detailsat
level 1
Approximation
Wavelet
Filter response/Coefficientof
perfect bandpass filter
Wavelet Behaving
as bandpass
Wavelet
Filter response/Coefficientof
Practically used wavelet filter
Collect the low frequencies
High frequencies
Wavelet Behaving
as bandpass
Wavelet
Filter response/Coefficientof
Practically used wavelet filter
Modular square ofThese transfer
function Add up to 1.
Prevent Loosing
signal/energy
To
Wavelet Behaving
as bandpass
Code Fragments to do the task
% Extract the level 1 coefficients.
a1 = appcoef2(wc,s,wname,1);
h1 = detcoef2('h',wc,s,1);
v1 = detcoef2('v',wc,s,1);
d1 = detcoef2('d',wc,s,1);
% Display the decomposition up to level 1 only. ncolors = size(map,1); % Number of colors.
sz = size(X);
cod_a1 = wcodemat(a1,ncolors);
cod_a1 = wkeep(cod_a1, sz/2);
cod_h1 = wcodemat(h1,ncolors);
cod_h1 = wkeep(cod_h1, sz/2);
cod_v1 = wcodemat(v1,ncolors);
cod_v1 = wkeep(cod_v1, sz/2);
cod_d1 = wcodemat(d1,ncolors);
cod_d1 = wkeep(cod_d1, sz/2);
image([cod_a1,cod_h1;cod_v1,cod_d1]);
axis image; set(gca,'XTick',[],'YTick',[]);
title('Single stage decomposition')
colormap(map)
pause
% Here are the reconstructed branches
ra2 = wrcoef2('a',wc,s,wname,2);
rh2 = wrcoef2('h',wc,s,wname,2);
rv2 = wrcoef2('v',wc,s,wname,2);
rd2 = wrcoef2('d',wc,s,wname,2);
Wavelet
Wavelet
Transfer function of
The wavelets
Transfer function of
The Scaling function
Wavelet
Want to understand The effect of this label
Have to perform convolution
Understand The effect of each this label
Wavelet
Graph 01: Transfer functions of the wavelet transforms
Works for Signals more then 8 samples 23= 8, Sample=8, level=3.
Level 1details
Level 2details
Level 3details
Level 4details
Level 5details
Transfer functions of
Approximation:The low pass
result That we keep at
the end
Wavelet
Graph 01: Transfer functions of the wavelet transforms
Leveldetails
+ approximation= 1
Property of wavelet
Wavelet
Approximation is a sinc- A perfect low pass filter
sincA-sincBA=A frequencyB=A frequency
-A perfect bandpass filter
Wavelet
Signal withmore than
eight samplesScenario:
Temper resolution : A vertical high-resolutionFrequency resolution : The sample frequency divided by the number of samples
Temper resolution>Frequency resolution
Increasingfrequency resolution
Decreasestemporal resolution.
Discrete Wavelet Transform(DWT)
Discrete Wavelet Transform(DWT)
Requires a wavelet ,Ψ(t), such that:- It scales and shifts
from an orthonormal basis of the square integral function.
)2/)2((2
1)(, jt
jt n
jnj
Scale Shift
Denote Wavelet
j and n both are integer
nmjlmlnj ., ,, To offer an orthonormal basis:)(, tnj
Orthonormal basis: A vector space basis for the space it spans.
.
.
Discrete Wavelet Transform(DWT)
Basis Function
Wavelets,ΨBasis function : An element of a particular basis for a function space
Scaling Function,Ψ
Discrete Wavelet Transform(DWT)
With each label:By shifting-
+
+
-
Shift
Inter-product is zero
Wavelets are orthogonal
Discrete Wavelet Transform(DWT)
Details at level 1 Scale factor , j =2, 22 =4
Discrete Wavelet Transform(DWT)
Details at level 2
Scale factor , j =1, 21 =2
Discrete Wavelet Transform(DWT)
Details at level 3
Scale factor , j =0, 20 =1
Discrete Wavelet Transform(DWT)
ApproximationLow
frequency
No Scale factor
Daubchies’ Wavelet (DW)
Daubchies’ Wavelet (DW)
•H()=high pass filter•D4=Daubchies’ Tap 4 Filter•Not symmetrical
Initial shape
Backward transformation of Wavelets
Opposite of forward transformationMirror the forward transformation on the right hand sideReplace the down-sampling by up-sampling.
Signal
Wavelettransform
of the Signal
Wavelettransform
of the Signal
Signal
Perfect step edge
JPEG Compression
15% lowestFourier coefficient=
Lowest 15 frequencyIs used to reconstruct the signal
Gibbs oscillation
Low pass version of the original image
JPEG Compression
15% largest scaleDaubchie’s coefficient=
JPEG Compression
JPEG Compression
Original signal
Wavelet coefficient(Symmlet wavelet)
ReconstructedThe 15% most important
coefficient=Getting fine output image
2D Wavelet Transform
Scaling function Wavelet
2Πk1 =ω1
2Πk2 =ω2
Low pass filter
2D Wavelet Transform
High pass filter
WaveletWavelet
Use Separable Transform
2D Wavelet Transform
Originalimage
hx = High pass filter(X-direction)
gx = low pass filter(X-direction)
Use Separable Transform
2D Wavelet Transform
hxy = High pass filter(y-direction)
Use Separable Transform
2D Wavelet Transform
gy = low pass filter(y-direction)
Use Separable Transform
2D Wavelet Transform
Use Separable Transform
2D Wavelet Transform
Further split
Use Separable Transform
2D Wavelet Transform
hy = High pass filter(y-direction)
Use Separable Transform
2D Wavelet Transform
hy = Low pass filter(y-direction)
Use Separable Transform
2D Wavelet Transform
Four region:
Blue= Diagonal Details at label 1
Green=Horizontal Details at label 1
Purple=vertical details at label 1
Yellow= Approximation at Label 1(Low pass in both x and y direction)
Use Separable Transform
2D Wavelet Transform
Doing the above steps recursively:Take the current approximation
Use Separable Transform
2D Wavelet Transform
Doing the above steps recursively:1. Take the current approximation2. And further split it up
Use Separable Transform
2D Wavelet Transform
Doing the above steps recursively:1. Take the current approximation2. And further split it up
Use Separable Transform
2D Wavelet Transform
New approximation
Doing the above steps recursively:1. Take the current approximation2. And further split it up3. Getting new approximation
Use Separable Transform
2D Wavelet Transform
Diagonal Details
Horizontal Details
vertical details
Approximation(can be furtherdecomposed)
In summary
Use Separable Transform
2D Wavelet Transform
In summary
Approximation(can be furtherdecomposed)
Use Separable Transform
2D Wavelet Transform
Visualization
Label ofapproximation
HorizontalDetails
HorizontalDetails
VerticalDetails
DiagonalDetails
VerticalDetails
DiagonalDetails
Use Separable Transform
2D Wavelet Transform
VisualizationLabel of approximation:• Very strong low pass filter• Few pixels
Use Separable Transform
2D Wavelet Transform
Visualization
Details in
Various Scale
Use Separable Transform
2D Wavelet Transform
Visualization
vertical details ->Shoulder
Horizontal Details ->Edges
Diagonal Details
Use Separable Transform
2D Wavelet Transform
Visualization
# of occurrences
Magnitudeof
coefficients
MostCoefficientHave valuesClose to zero
Use Separable Transform
2D Wavelet Transform
Graph from the histogram
# of occurrences
Magnitudeof
coefficients
DiscardCoefficient
valuesClose to zero
Use Separable Transform
2D Wavelet Transform
More precise
Visualization
Original image:Gray square on a Black Background
Diagonal Details
Horizontal Details(row by row)
Vertical details(column by column)
Use Separable Transform
2D Wavelet Transform
Toy of original image
Use Separable Transform
2D Wavelet Transform
Decomposition at Label 4
Original image
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
CT vs. DWT
DWT Target Goal:1.Applying a DWT to decompose a digital mammogram into different subbands.
2.The low-pass wavelet band is removed (set to zero) and the remaining coefficients are enhanced.
3.The inverse wavelet transform is applied to recoverthe enhanced mammogram containing microcalcifications [7].
7. Wang T. C and Karayiannis N. B.: Detection of Microcalcifications in Digital Mammograms Using Wavelets, IEEETransaction on Medical Imaging, vol. 17, no. 4, (1989) pp. 498-509
The results obtained by the Contourlet Transformation (CT)are compared with
The well-known method based on the discrete wavelet transform
Plan-of-Action
For microcalcifications enhancement :
We use-The Nonsubsampled Contourlet Transform(NSCT) [12]
The Prewitt Filter.
12. 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
Plan-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[6].
6. 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)
Method
The proposed method is based on the classical approach used in transform
methods for image processing.
1. Input mammogram
2. Forward NSCT
3. Subband Processing
4. Inverse NSCT
5. Enhanced Mammogram
Figure 01: Block diagram of the transform methods for images processing.
Method
NSCT is implemented in two stages:
1. Subband decomposition stage
2. Directional decomposition stages.
Details in upcoming slides
Method
1. Subband decomposition stage
For the subband decomposition:- The Laplacian pyramid is used [13]
Decomposition at each step:-Generates a sampled low pass version of the original-The difference between :
The original image and the prediction.
13. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis and classification,Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol. 3, (1999) pp.1417-1420
Details ……..
Method
1. Subband decomposition stage
Details ……..
1. The input image is first low pass filtered
2. Filtered image is then decimated to get a coarse(rough) approximation.
3. The resulting image is interpolated and passed through a Synthesis
flter.
4. The obtained image is subtracted from the original image :
To get a bandpass image.
5. The process is then iterated on the coarser version (high resolution)of the image.
Plan of Action
Method
2.Directional Filter Bank (DFB)
Details ……..
Implemented by using an L-level binary tree decomposition :
resulting in 2L subbands
The desired frequency partitioning is obtained by :
Following a tree expanding rule
- For finer directional subbands [13].
13. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis and classification,Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol. 3, (1999) pp.1417-1420
The Contourlet Transform
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 01: (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)
DenoteEach subband by yi,j
Wherei =decomposition level and J=direction
The Contourlet Transform
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.
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 an operator (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….
In order to obtain a binary image:A threshold Ti,j for each subband is calculated.
The threshold calculation is based:-When mammograms are transformed into the CT domain.
The microcalcifications appear :
On each subband Over a very
homogeneous background.
Most of the transform coefficients:-Are grouped around the mean value of
the subband correspond to the background
-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
Details….
Exhaustive tests:-Consist on evaluating subjectively a set of 15 different mammograms
-With Different combinations of values,
The weights W1, and W2 are determined:-Selected 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.
A conservative threshold of 3σi;j is selected:where σi;j is the standard deviation of the corresponding subband y I,j .
Metrics
To compare the ability of :
Enhancement achieved by the proposed method.
Why?
1. Distribution Separation Measure (DSM),
2. The Target to Background Contrast enhancement (TBC) and
3. The Target to Background Enhancement Measure based on Entropy (TBCE) [14].
Measures used to compare:
14. 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. Distribution Separation Measure (DSM) Measures used to compare:
The DSM represents :How separated are the distributions of each mammogram
…………………………(3)DSM = |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. Target to Background Contrast Enhancement Measure (TBC).
Measures used to compare:
The TBC Quantifies :The improvement in difference between the background and the target(MC).
…………………………(4)
0
E
0
0
E
E
µucalc
µucalc
µtissue
µucalc
µtissue
µucalc
TCB
Defined by:
Where:
Eµucalc
0µucalc
= Standard deviations of the microcalcifications region in the enhanced image
= Standard deviations of the microcalcifications region in the original image
Metrics
3.Target to Background Enhancement Measure Based on Entropy(TBCE)
Measures used to compare:
The TBCE Measures :- An extension of the TBC metric- Based on the entropy of the regions rather
than in the standard deviations
Defined by:
Where:
…………………………(5)
0
E
0
0
E
E
µucalc
µucalc
µtissue
µucalc
µtissue
µucalc
TCB
= Entropy of the microcalcifications region in the enhanced image
= Entropy of the microcalcifications region in the original image
Eµucalc
0µucalc
Experimental Results
Experimental Results
(a)Original image (b)NSTC 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.
DMS, TBC and TBCE metrics on the enhanced mammograms
Experimental Results
NSCT Method DWT Method
DSM TBC TBCE DSM TBC TBCE
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.
DMS, TBC and TBCE metrics on the enhanced mammograms
Experimental Results Analysis
0
0.2
0.4
0.6
0.8
1
1.2
TBC
Mammogram
TBC Matrix
NSCT DWT
The proposed method gives higher results than the wavelet-based method.
DMS, TBC and TBCE metrics on the enhanced mammograms
Experimental Results Analysis
0
0.2
0.4
0.6
0.8
1
1.2
TBC
E
Mammogram
TBCE Matrix
NSCT DWT
The proposed method gives higher results than the wavelet-based method.
DMS, TBC and TBCE metrics on the enhanced mammograms
Experimental Results Analysis
0
0.2
0.4
0.6
0.8
1
1.2
DSM
Mammogram
DSM Matrix
NSCT DWT
The proposed method gives higher results than the wavelet-based method.
Experimental Results AnalysisMesh plot of a ROI containing microcalcifications
(a)The original mammogram
(mdb252.bmp)
(b) The enhanced mammogram
using NSCT
Experimental Results Analysis
(a)The original mammogram
(mdb238.bmp)
(b) The enhanced mammogram
using NSCT
Experimental Results Analysis
(a)The original mammogram
(mdb253.bmp)
(b) The enhanced mammogram
using NSCT
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
Plan of action as follows:
1. Segment the microcalcification(MC) from the enhanced image.
2. Find an attribute based on which I can train the machine
2. Based on feature(size/shape), will move on to classification( benign or malignant)
Reference
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Reference
10. Do M. N. and Vetterli M.: The Contourlet Transform: An efficient DirectionalMultiresolution Image Representation, IEEE Transactions on Image Processing, vol.14, (2001) pp. 2091-2106
11. Da Cunha A. L., Zhou J. and Do M. N,: The Nonsubsampled Contourlet Trans-form: Theory, Design, and Applications, IEEE Transactions on Image Processing,vol. 15, (2006) pp. 3089-3101
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