Mahua Mam Iiitm Gwalior.1

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Discrimination forMalignant and Benign

Masses in Breast UsingMammogram:

A Study on

Adaptive Neuro-FuzzyApproaches Mahua Bhattacharya

AB !ndian !nstitute of !nformation

"echnology # Management$

%&alior


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'rigin of "he (or)

X-Ray mammography is the most important modality

which is used in early detection of breast cancer. Women

have better chance to survive if breast cancer is detected

early. The failure to detect any abnormal lesion at an early

stage may lead to disastrous consequences, so the

improvement of mammographic image quality is essential

for breast cancer screening. Therefore a need exists to

automate the process of analyzing a large number of

mammograms and to discriminate the benign lesion frommalignant one for proper therapy planning.


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'rigin of "he(or)*cont++,

The present work is continuation of earlier work based on theory of shape related to gradation of benignancy of tumor in tissue region. Theconcept of symmetry analysis of shape computing a distancefunction D between the contour of the tumor model and thepattern tumor lesion was utilized to classify the tumors mainly intwo broad categories benign and malignant transformations.

References

M. Bhattacharya, D. Dutta Majumder, “Knowledge Based Approach to Medical Image Processing” inPattern Directed Information Analysis(Algorithms, Architecture & Applications, publisher : NewAge Wiely, 2007. (in press)

M.Bhattacharya and D.Dutta Majumder, “Breast Cancer Screening UsingMammographic Image Analysis' Sixteen International CODATA (France) Conference (8-12 Nov ,Delhi (1998).

M.Bhattacharya, “Development of Mathematical Model for Radiographic Image Analysis (published,!!"# International $ournal of %omputational and &umerical Analysis and Applications, Academic'ublication ( !!"#.

D.Dutta Maumder ) Mahua Bhattacharya, “Cybernetic Approach To Medical Technology: Application To Cancer Screening And Other Diagnostics”, Millennium *olume of +ybernetes,International $ournal of ystems ) %ybernetes, M%B publications -+, *ol. , number "/0 , pp10"2304, !!! (5ran6 7eorge Research a8ard 9inning 'aper #.


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(hat is Mass

Masses are three-dimensional lesions which may

represent a localizing sign of Breast Cancer .

They are described by their Size, Shape, Margincharacteristics, X-ray attenuation, Effects on

surrounding tissues etc.

Depending on the Morphologic criteria of themass, the lielihood of Benignancy/ Malignancy

can be established.

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Masses *cont++,

" mass shape may ha#e one of fi#e characteristics$ Round,

Oval, o!ulated, "odular and Stellate. The descriptions

are fairly self-e%planatory, and a schematic picture of each

shape is shown below.

"long with &hape, Margin and &ize of the masses are also

important indicators to determine Benignancy/ Malignancy#

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'./ective of "he (or)

(b)ecti#e of present wor is de#elop a soft-co$puting !ased "euro %uzzy &y!rid Model which will be robust enough to tae decision

whether the lesion is belonging to either benigngroup or in malignant group.

*urther ob)ecti#e is not only to predict the+enignancy Malignancy of lesion but to predict

tendency of growth of the lesion either towardsbenign or malignant i.e. to de#elop a GradationTechnique for prognosis of disease for moreaccurate therapeutic planning .

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Preprocessing of Acquired Images

0oises appear in the images as abrupt change of grayle#els, thus a#erage of the pi%els contained in theneighborhood of the filter mas is the easiest method fornoise remo#al. These filters are sometimes called a#eragingfilters.

1n this study 33 smoothing filters used to remo#e irrele#antdetails. The structure of this filter shown below.

+y replacing the #alue of

e#ry pi%els of image by thea#erage of the mas reduces

sharp transitions in gray

le#el.

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Segmentation of Masses

The presence of high-contrast fibroglandular tissues in themammograms may confuse the radiologists as the actualcalcified masses.

1n cases where the breast may be dense, the margins

may appear obscured because both benign and malignantlesions de#elop in lobules which are hea#ily surroundedby normal e%tralobular connecti#e tissue. Thus properseg$entation of masses in the dense breastmammograms is a difficult, important and challenging

tas.

The final classification on degree of malignancy of themasses also depends on the superiority of thesegmentation techni5ue.

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Segmentation of Masses*cont++,

This mass is diagnosed as being Malignant, if not properly

segmented. Thus segmentation of mass boundary plays an

important role in discrimination.

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Due to the superimposition of three-dimensional tissue structures, the margins of a mass may be

obscured and looed ill-defined. " mass

with almost o#al shaped circumscribed margin

obscured by the surrounding tissues and its proper segmented contour shown below.


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Segmentation of Masses .yFuzzy c-Means 1lustering

"echni2ue *uzzy 6-means clustering algorithm used forintensity based segmentation of Masses.

6luster center A represents the healthy breast tissue.

6luster center B represents false presence of masses.

6luster center C represents actual mass region.

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1nitialize the membership matri% " with random #alues between and as shown in e578.

78

6alculate c fuzzy cluster centers using e5728.

728

Fuzzy 1-Mean 1lusteringAlgorithm

2#(2

=∑=

x A k C

ii

∑

∑

=

==n

k

m

N

k k

m

i

x A

x x A

k i

k i

2

2

#:(;

#:(;

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Fuzzy 1-Mean 1lusteringAlgorithm *1ont++, 6ompute the cost function according to 95 738. &top if its

impro#ement o#er pre#ious iteration is below a threshold.

738

6ompute a new membership matri% A using 95 748.

748

∑=

−

=

C

!

m

k i

d d

x A

!k

ik

2

#2(

#(

2#(


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Superiority of F1M over

1anny "echni2ue

(a# (b# (c# (d#

7a8 (riginal Mammogram, 7b8 6utout from suspicious region,

7c8 &egmentation using *6M, 7d8 &egmentation using 6anny.

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Boundary Feature3epresentation

The degree of correct classification strongly depends on

6lassic *eature &election Method.

Thus to describe the boundary #ery precisely, we apply

%ourier 'escriptor method in our proposed algorithm. 1ncase of abrupt change in boundaries, *ourier descriptors

contains high fre(uency components 7a stellate mass

contains #ery high fre5uency components8.

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Fourier Descriptors 'f "heBoundary

6onsider the boundary consists of -points in the %-y

plane staring at an arbitrary point 7%,y

8 to the

coordinate pairs 7%,y

8, 7%

2,y

28,:..7%

-,y

-8 .

9ach co-ordinate pair can be treated as a comple%

number so that

s)*+ )*+ . 0 y)*+

for ;, , 2, :.., − .

The greatest ad#antage of this type of representation

is

that it reduces the 1-' pro!le$ into 2-' pro!le$.IICAI-07


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"e4tural Feature3epresentation

Te%ture information plays an important role in imageanalysis and understanding. 1t measures the RelativeS$oothness, 6verage 7nifor$ity, 8ray level 9aria!ility of the images.

(nly boundary of the masses sometimes representmisleading information 7due to limitations of filmmammography, a circumscribed mass appeared asmalignant one8 thus blending of te%ture features withboundary impro#e the classification results.

1t is noted that, a benign mass posses S$ooth and7nifor$ te%ture, it is an indicator to determine the degreeof $alignancy/!enignancy.

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"e4tural Feature3epresentation *cont++,

Te%tural *eatures are described on the basis of statistical

properties of the gray le#el image.

1f z be a random #ariable denoting the gray le#els in the range

of 7, > − 8.

The nth moment of z about the mean is

where m is the mean #alue of z,

∑ −−

=

=2

!#(#(#(

%

& n ' pm ' & &

n ' µ

∑ ×−

=

=2

!#(

%

& ' p 'm & &

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"e4tural Feature3epresentation *cont++,

1nd moment 7n18, the 9ariance µ1 )z+ is a measure of relati#e

smoothness of the gray le#el contrast.

:rd moment 7n:8, measure of S*e;ness of the histogram, is

useful for determining the degree of symmetry of histograms.

"nother useful te%ture measurement is based on histograms?7nifor$ity@778 gi#en by

"#erage Entropy is a measure of gray-le#el #ariability. 1t isdefined as

#(2

!

' p & %

&

( ∑−

=

=

#(#( log

2

! ' ' & &

%

&

p pe ∑−

=

=

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1lassi5cation of Features

Ae ha#e introduced a robust ?6dapti#e "euro %uzzy

Model for classification of features into benign and

malignant stages.

1t is an 5nnovative Soft Co$puting approaches that

tacles the uncertainties present in the system. "s a

result the decision maing by the e%pert system is

more close to reality.

&y!rid-earning rule has been used to train the

classifier.

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6y.rid 7earning 3ule:

Bybrid leaning rule combines Steepest 'ecent

method and east-S(uares Esti$ator for fast

identification of parameters.

&ummary of the rule gi#en below < < %or;ard pass < Bac*;ard pass

=re$isepara$eters

%ied 8radient descent

Conse(uentpara$eters

east s(uaresesti$ator

%ied

Signals "ode outputs Error signals

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Advantages of 6y.rid7earning

Hybrid leaning rule combines steepest decent methodand least-squares estimator for fast identification ofparameters in adaptive Neuro-Fuzzy model.

The hybrid method converges much faster than asingle Neural or Fuzzy approach since it reduces thesearch space dimensions of the original pure backpropagation learning.

The hybridization of Neuro-fuzzy approaches isrobust, adaptive and handles uncertainties present inthe features much better than a conventional neuralnetwork.

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Searching of Feature

su.set using F1M

+oundary detection of masses based on %ourier

'escription method, implies a large number of

feature #ectors.

Ae introduce %uzzy C-$eans Clustering techni5ue

7%CM8 to reduce the shape descriptors into four

clusters only.

The ob)ecti#e of *6M is that degree of association isstrong for descriptors within the same cluster and

wea for the descriptors in different clusters.

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Decision Ma)ing 7ogic

Ae ha#e defined a distance function 7µ8 such thatµ1 = D1 O1

µ2 ; C7'2 O2837'2 O28.!

; C7D − (8 2 .!

1t determines the de#iation from roundness of themasses 7+enign &tage8. The degree of malignancy ishigher for higher #alue of µ.

Ae ha#e set the Decision Eule as,

1f µ2 F; 2, Decision ?Benign>

1f 2 F; µ2F; 4, Decision ?3endency to;ards Malignancy@

1f µ2 G 4, Decision ?Malignant@

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Decision Ma)ing 7ogic*cont++,

Ae ha#e defined another distance function 7µ28 asµ1 ; '1 O1

µ1 ; C7'1 O1837'1 O18.!

; C7D2 − (28 2 .!

1t determines the de#iation of smoothness of themasses 7from +enign &tage8. The degree ofmalignancy is higher for higher #alue of µ2.

Ae ha#e set the Decision Eule as,1fµ1 F; !, Decision ?S$ooth>

1f !F;µ1F;3, Decision ?3endency to;ards Roughness@

1fµ1 G 3, Decision ?Coarse@

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Decision Ma)ing 7ogic*1ontd++,

The *inal Decision on Degree of Malignancy is gi#en below$

+enign H &mooth

Benign Stage

+enign H Eough

+enign H 6oarse

Tendency towards Malignant H &mooth

Tendency towards Malignant H Eough 3endency 3o;ards Malignant Stage

Malignant H &mooth

Tendency towards Malignant H 6oarse

Malignant H Eough =ossi!ly in Malignant Stage

Malignant H 6oarse

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84perimental 3esults

Ae ha#e applied the proposed algorithm to

databases consisting of 1?? images.

The classifier was first trained with ob#ious Benign

Masses as identified by the e%pert radiologists.

The non-ob#ious cases ha#e been tested and

classified during the e%periment.

*ew of the non-ob#ious case studies and final

decision on Benignancy/ Malignancy are gi#en in

the ne%t slides$

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84perimental 3esults*cont++,

'ecision$

Tendency towards

Malignant &tage

µ;49.9, µ2;13.48

'ecision$ Iossibly in

Malignant &tage

µ;47.42, µ

2;29.33

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84perimental 3esults *cont++,

'ecision$ Iossibly

in Malignant &tage

µ;49.58, µ2;28.48

'ecision$ Tendency

towards Malignant

&tage

µ;48.72, µ

2;10.96

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84perimental 3esults *cont++,

'ecision$

Tendency towards

Malignant &tageµ

; 2.59, µ

2;32.38

'ecision$ Iossibly in

+enign &tage

µ; 18.45, µ

2;17.86

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1onclusions

1n proposed methodology, we ha#e attempted tode#elop a techni5ue based on "dapti#e 0euro *uzzymodel by e%tracting the boundary of the lesion orregion of interest E(1 and also te%ture features.

The proposed classifier has been trained by hybrid-learning rule with ma%imum fifty epochs. The learningprocess is continued till the performance goal

reaches. The output node #alues indicate the De#iation or

Distance function of the test masses with respect tothe trained benign masses.

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1onclusions *cont++,

1t is also noted that there is no sharp boundary

among the three stages − Benign, 3endencyto;ards Malignancy and Malignant Stage#

The partition among the stages is also %uzzy

=artition#

Thus when we tae a decision that a mammographic

masses belongs to the ?Benign stage@, there is a

little chance that it actually belongs to ?3endency

to;ards Malignant Stage@ and vice versa.

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Ac)no&ledgment

Ae would lie to than to 'r# S# @# Shar$a

of 9=( J-ray and 1maging 1nstitute, =olatafor pro#iding radiological e%pertise.

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3eferences

1. L. M. Bruce and R. R. Adhami, “Classifying Mammographic Mass Shapes Using theWavelet Transform Modulus-Maxima Method ”, IEEE Trans. Medical Imaging, vol. 18, no.

12, pp. 1170-1177, (1999).

2. J. K. Kim and H. W. Park, “Statistical Textural Features for Detection of

Microcalcifications in Digitized Mammograms”, IEEE Trans. Medical Imaging, vol. 18,

no. 3, pp. 231-238, (1999).

3. M.Bhattacharya and D.Dutta Majumder, “ Breast Cancer Screening Using Mammographic

Image Analysis”, 16th International CODATA (France) Conference, (8-12) Nov. Delhi

(1998).

4. M. Bhattacharya and A. Das, “Fuzzy logic Based Segmentation of Microcalcification in

Breast Using Digital Mammograms Considering Multiresolution”, Proc. of International

Machine Vision and Image Processing Conference (IMVIP-07), in NUI Maynooth, Co,

Kildarem Ireland, UK by IEEE Computer Society, pp. 98-105, 5 -7 Sept, (2007).

5. Mahua Bhattacharya, “A Computer-Assisted Diagnostic Procedure For Digital

Mammograms Using Adaptive Neuro Fuzzy Soft Computing” accepted IEEE Nuclear

Science Symposium, Medical Imaging Conference, San Diego, California USA , 29 th Oct

-4 th Nov , (2006).

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