microcalcification identification in digital mammogram for early detection of breast cancer

“The best protection is early detection”

Microcalcification identificationMicrocalcification identificationin digital mammogram for earlydetection of breast cancerMasters -1 Presentation

N hid AlNashid AlamRegistration No: [email protected]

Supervisor: Dr. Mohammed Jahirul Islam

Department of Computer Science And EngineeringShahjalal University of Science and TechnologyTuesday, April 29, 2014

Introduction

Breast cancer:The most devastating and deadly diseases for women.

Steps to control breast cancer:W ill h i1) Prevention

2) Detection3) Diagnosis4) T t t

We will emphasis on :1) Detection2) Diagnosis

Computerize Breast cancer Detection System:

4) Treatment

o Computer aided detection (CADe) o Computer aided diagnosis (CADx) systems

Micro-calcification

Mammography

Mammogram

Micro-calcificationMicro‐calcifications :

Tiny deposits of calcium

Position:1. Can be scattered throughout the mammary gland orthe mammary gland, or 2. Occur in clusters.

They are caused by a number of reasons:y y

Aging ‐ The majority of diagnoses are made in women over 50

G ti I l i th BRCA1 (b t 1 l t) d BRCA2 (b t 2Genetic ‐ Involving the BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset) genes

Depending on what pattern the micro‐calcifications present determines :p g p pThe 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 :

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

USE:I. Viewing x‐ray imageII. Manipulate X‐ray image on a computer screen

Mammogram

Mammogram:Amammogram is an x‐ray picture of the breast

Use:Use:To look for changes that are not normal.

Result Archive:mdb226.jpg

Result Archive:The results are recorded on x‐ray film or directly into a computer

Types of mammograms: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.

Problem Statement

Problem StatementMain challenge :

QUICKLY AND ACCURATELY overcome the development of breast cancerQUICKLY AND ACCURATELY overcome the development of breast cancer

Reason behind the problem: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:Si il h t i tiSimilar characteristics:

Abnormal and normal microcalcification

Problem Statement

The signs of breast cancer are: Masses CalcificationsCalcificationsTumorLesionL

Individual Research Areas

Lump

A k f h i i i lA key area of research activity involves :Developing better ways‐

To diagnose and stage breast cancer.

GOAL

• Early detection of Breast Cancer.

-Micro-calcification detection-Micro-calcification detection

The Micro‐calcification:Occur in clustersOccur in clusters

The clusters may vary in size from 0.05mm to 1mm in diameter.

Variation in signal intensity and contrastVariation in signal intensity and contrast.May located in dense tissue

Difficult to detect.

• Develop a logistic model:

‐To determine the likelihood of‐To determine the likelihood of CANCEROUS AREA

from the image values of mammograms.

Why our work is important?Why our work is important?

B tt C S i l R t (E l D t ti )‐Better Cancer Survival Rates(Early Detection ).

‐The diagnostic management of breast cancer (a difficultg g (task)

‐‐Radiologist fails to detect Breast Cancer.Radiologist fails to detect Breast Cancer.

‐Computerized decision support systems provide“second opinion” :

Fast,R li bl dReliable, andCost‐effective

Literature ReviewLiterature Review

To detect micro-calcifications in an automatic manner-A number of methods have been proposed

These include:These include:

Global and local thresholding

Statistical approaches

Neural networks

Fuzzy logicFuzzy logic

Thresholding of wavelet coefficients and related techniques.


Camilus et al.[1] propose an efficient method

To identify pectoral mussel using:

Watershed transformation

Merging algorithm to combine catchment basinsMerging algorithm to combine catchment basins

MIAS database(84 mammograms)


Pronoj et al.[2] reviews on :j [ ]Exiting approach of preprocessing techniques:

Thresholding techniquesBoundary based method G lBoundary based methodHybrid techniquesWatershed transformation

Goal:oTo improve quality of imageoFacilate further processingEdge detection:

SobelPrewitt

oFacilate further processingoRemove noiseoRemove unwanted part

RobertsLaplacian of GaussianZero‐cross

pfrom the background

Zero‐crossCanny

Literature Review

Oliver et al.[3] worked on:

Literature Review

Oliver et al.[3] worked on:Local feature extraction from a bank of filters.

Performs training steps:‐To automatically learn and select:

Th f t f i l ifi tiThe features of microcalcifications.

GoalGoal:oTo obtain different microcalcification morphology

Literature Review

Oliver et al.[4] :

Literature Review

Oliver et al.[4] :Detection based on:

Learning variation in microcalcifications morphologyLocal image features‐

Set of feature is trained a pixel‐based b ti l ifiboosting classifier

Pixel‐based boosting classifier:At each round automatically selects the mostAt each round automatically selects the most

salient microcalcifictions features.Goal:

oDetect microcalcification and cluster

Literature Review

Oliver et al.[4] :

Literature Review

Oliver et al.[4] :

Testing new mammogram:Only salient fractures are computed

Mi l ifi ti l t f dMicrocalcification clusters are found:By inspecting the local neighborhood of

each microcalcificationeach microcalcification.

Literature Review

Papadopoulus et al.[5] :

Literature Review

Papadopoulus et al.[5] :

Microcalcification detection using neural network

Preprocessing image enhancement

Got best result by applying:The local range modification algorithmThe local range modification algorithmRedundant discrete wavelet linear stretching

and shrinkage algorithm.g g

Literature Review

Pal et al.[6] :

Literature Review

Pal et al.[6] :To detect microcalcification cluster used:

oWeighted density function:‐Position of microcalcifications

(take into account)U dUsed:

oMulti‐layered perception network for selecting 29 features29 features

Features are used :To segment mammograms

Literature Review

Razzi et al [7] proposed :

Literature Review

Razzi et al.[7] proposed :

A two‐stage decomposition wavelet filteringA two stage decomposition wavelet filteringFirst stage:Reduce background noiseg

Second stage:A hard thresholding technique:

‐To identify microcalcificationCl t id d if th 3 i l ifi tiCluster was considered if more then 3 microcalcifications were

detected in a 1cm2 area

Literature Review

Yu et al.[8] proposed :

Literature Review


For clustered microcalcification detection used Combined :

‐Model‐based and statistical texture features

Firstly:Suspicious region containing microcalcification wereSuspicious region containing microcalcification were

detected using‐Wavelet filter and two thresholds

Literature Review


Literature Review


Secondly:Textural features were extracted:

‐From each suspicious regionF t l ifi d bFeatures classified by:

‐A back propagated neural network

Texture features based on both:oMorkov random fields and oFractal models

Why our work is different?Why our work is different?

Automatically detectmicro‐calcifications

No existing methods give full satisfaction andclinically acceptable results.clinically acceptable results.

Will propose wavelet based technique toWill propose wavelet based technique todetect micro‐calcifications automatically.

Schematic representation of the system MLO View Mammogram

Image Denoising

Image Segmentation (K-means Clustering) Draw a vertical line:

Connecting points P1 and P3Draw an angular lineImage Morphology

Image EnhancementHistogram Equalization

Region Separation(Vanishing Ligament and Fatty Tissue Area)

Binarisation of Segmented Image(Otsu’s Thresholding) Flood Filling Within the

T i l A

Draw an angular lineConnecting points P3 and P2

Finding 3 Points ofPectoral Muscle Triangle

Contrast Enhancement

Convert to binary image using Thresholding algorithm

(Otsu s Thresholding)

pect

oral

el

Triangular Area

Subtracting the pectoral muscle region from original image

S i i l i i Pectoral Muscle Removed Image

Thresholding algorithm

Finding Biggest Blob

Hole Filling Inside The Biggest Blob Tria

ngle

of p

mus

se

Microcalcification Detection

ROI SegmentationFind the first black point, P2 of

Start scanning pixel intensity value to find the first white

seeding point, P1

Hole Filling Inside The Biggest Blob

Keep the Largest Blob and Discard Other Blobs from the Original

Mammogram X-Ray ImageL b l R d X R

Feature Extraction

Feature Selection(B i M li t)

pfirst row of the image

Draw a horizontal line connecting two points P1 and P2

Label Removed X-Ray ImageHistogram Equalization

(Contrast Stretching)

(Benign or Malignant)

Cancer Detection

g p

Find the First Black Point of first column, P3

Gantt Chart

Chart 01: Gantt Chart of this M.Sc thesis showing the duration of task against the progression of time

Materials and ToolsMaterials and Tools

Matlab 2001a

Database: MIASDatabase: MIAS

Database: MIAS databasehttp://skye.icr.ac.uk/miasdb/miasdb.html

Database: MIAS Databasehttp://skye.icr.ac.uk/miasdb/miasdb.html

Mammography Image Analysis Society (MIAS)

Class Of Severity Of

Mammography Image Analysis Society (MIAS) ‐An organization of UK research groups

Class Of Abnormality

Severity Of Abnormality

1 Calcification(25)

The Location Of The

Center Of

(25)

1.Benign(Calc‐12)

2 Circumscribed Masses

3 Speculated Massesmdb223.jpg mdb226.jpg

Center Of The

Abnormality And Its

3 Speculated Masses

4 Ill‐defined Masses

5 Architectural Diameter. Distortion

2.Malignant(Cancerous)(Calc‐13)

6 Asymmetry

7 mdb239.jpg mdb249.jpg(Calc 13)

NormalFigure01:X‐ray image form MIAS database

Database: MIAS Databasehttp://skye.icr.ac.uk/miasdb/miasdb.html

Mammography Image Analysis Society (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

Internal Breast Structure

Image SegmentationGoal: Removing X‐ray Labeling And Pectoral muscles

Image Segmentation K‐means Clustering

Goal: Removing X‐ray Labeling And Pectoral musclesg y g

Why Segmentation?

Partitioning a digital image into multiple regions (sets of pixels).

GOAL OF SEGMENTATION:T l t bj t d b d i (li t ) i

(C) Final Segmented Image

• To locate objects and boundaries (lines, curves, etc.) in images.

• Result of image segmentationA set of regions that collectively cover the entire image (a)

(intensity <130) (intensity >200)

2

3

‐A set of regions that collectively cover the entire image. (a) ‐A set of contours extracted from the image. (C)

• Each of the pixels in a region(1, 2, 3) are similar with respect to some

1

(a) Segmentation PartEach of the pixels in a region(1, 2, 3) are similar with respect to some characteristic or computed property, such as color, intensity, or texture.

• Adjacent regions(1, 2, 3) are significantly different with respect to the h i i ( )

( ) g

same characteristic(s).

(b)Original image



Proposed framework for breast profile segmentation


Goal: Removing X‐ray Labeling And Pectoral muscles

Plan of Action:

(intensity <130) (intensity >200)

g y g

Separating the Pectoral muscle

1. Original Image 2. Segmentation Part3. Final Segmented Image

(K-means clustering)(mdb256.jpg) BINARY Thresholding

Keeping the

5.Final Segmented Image

ThresholdingFor Two Different Ranges

Keeping the biggest Cluster

4. Binary ImageLactiferous Sinus, Ducts, lobule

(After removing pectoral muscles, fatty tissues, Ligaments)

(a)Without Noise (b)With Noise

Noise Removing

Image Morphology:


Image Morphology:‐Deals with the shape (or morphology)of features in an image‐Operate on bi‐level images

1.Morphological Analysis:Th B i O ti

Techniques:Operate on bi level images

The Basic Operations are ‐I.EROSIONII.DILATION

Lactiferous Sinus Ducts lobule


Using the basic operations we can perform ‐a)OPENINGb)CLOSING

Lactiferous Sinus, Ducts, lobule(After removing pectoral muscles, fatty tissues, Ligaments)

Advanced Morphological Operation can then be implemented using Combinations Of All Of These

2.Image Smoothing/Filtering(Low pass):‐Averaging (Drawback: Can vanish interesting details)

More On Image Morphology Later

Structuring Elements Hits & Fits

Image Morphology Noise RemovingStructuring Elements, Hits & Fits

B Structuring Element

Fit All i l i h iFit: All on pixels in the structuring element cover on pixels in the image

AC

g

Hit: Any on pixel in the structuring element covers an on pixel in the image

All morphological processing operations are based on these simpleAll morphological processing operations are based on these simple ideas


Image Morphology Noise Removing

Structuring elements can be any size and make

Structuring Elements, Hits & Fits

Structuring elements can be any size and make any shape

However for simplicity we will use rectangularHowever, for simplicity we will use rectangular structuring elements with their origin at the middle pixelmiddle pixel

1 1 10 0 1 0 00 1 1 1 00 1 0

1 1 1

1 1 1

0 1 1 1 01 1 1 1 10 1 1 1 0

1 1 1

0 1 01 1 1 0 1 1 1 00 0 1 0 0

0 1 0


Image Morphology Noise Removing

0 0 0 0 0 0 0 0 0 0 0 01 1 1


0 0 0 1 1 0 0 0 0 0 0 00 0 1 1 1 1 1 0 0 0 0 0B C

1 1 1

1 1 1

1 1 10 1 1 1 1 1 1 1 0 0 0 00 1 1 1 1 1 1 1 0 0 0 0

1 1 1Structuring Element 1

0 0 1 1 1 1 1 1 0 0 0 00 0 1 1 1 1 1 1 1 0 0 0

0 1 0

1 1 10 0 1 1 1 1 1 1 1 1 1 00 0 0 0 0 1 1 1 1 1 1 0

A1 1 1

0 1 0Structuring 0 0 0 0 0 1 1 1 1 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0Element 2


Image Morphology Noise Removingg ,

•The structuring element is moved across every pixel in the original image to give a pixel in a new processed image(very like spatial filtering)

•The value of this new pixel depends on the operation performed•The value of this new pixel depends on the operation performed

•There are two basic morphological operations:

Erosion and Dilation


Noise Removing 1. Morphological Analysis

ErosionStructuring Elements, Hits & Fits

Erosion of image f by structuring element s is given by f sThe structuring element s is positioned with its origin at (x, y) and the new pixel value is g ( , y) pdetermined using the rule:

⎧ fiif1 f

⎩⎨⎧

=otherwise0

fitsif1),(

fsyxg

⎩A morphological opening of an image is an erosion followed by a dilation

What Is Erosion For?


What Is Erosion For?

Erosion can split apart joined objectsp p j j

Original imageErosion by

3*3square

Erosion by 5*5

square

Erosion can split apart

squarestructuring element

square structuring element

%noise removingse = strel('disk',25);for i=1:19erode_bolb = imerode(largest_bolb,se);end

Watch out: Erosion shrinks objects

Noise Removing

5 Fi l S d I

1. Morphological Analysis



Lactiferous Sinus, Ducts, lobule(After removing pectoral muscles, fatty tissues, Ligaments)

Erosion Example


p

Original Image Processed Image With Eroded Pixels

Structuring Element

Erosion ExampleOriginal Image Processed Image

Structuring Element

Image Morphology X-ray Label RemovingNoise Removing 1. Morphological Analysis


DilationDilation of image f by structuring element s is


Dilation of image f by structuring element s is given by f sThe structuring element s is positioned with its

⊕

The structuring element s is positioned with its origin at (x, y) and the new pixel value is determined using the rule:determined using the rule:

⎨⎧ hits if 1

)(fs

bw_image = im2bw(Binary_image);imtool(bw_image)se1 = strel ('line', 3,0);se2 = strel ('line', 3,90);

⎩⎨= otherwise 0

),( yxg for i=1:9BW2= imdilate (bw_image, [se1

se2], 'full')BW2 = imfill(BW2,'holes');

A morphological closing of an image is a dilation followed by an erosion

end

Dilation Example


Original Image Processed Image

p

Structuring Element

Dilation ExampleOriginal Image Processed Image With Dilated Pixels

Structuring Element

Dilation ExampleDilation ExampleOriginal image

Hole fillingInside the blob(dilation)

Result image(Label Removed)Binary image

mdb240.jpg

%hole filling with in the bolbse = strel('disk',39);for i=1:19closeBW_largest_bolb = imclose(largest_bolb,se);

A morphological closing of an image is an dilation followed by a erosion

Noise Removing 2.Image Smoothing/Filtering(Low pass):

After Removing Some NoiseImage Containing Noise(mdb041.jpg)(mdb041.jpg)

Noise Removing

Chosen Technique 2D MEDIAN FILTERING FOR SALT AND PEPPER NOISEChosen Technique 2D MEDIAN FILTERING FOR SALT AND PEPPER NOISE

After Removing Some NoiseImage Containing Noise(mdb041.jpg)

I = medfilt2(I, [1 5]); Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. Since all the mammograms are in high quality images, there is no need to perform median filtering

Why choosing?

1. Morphological AnalysisOVER

2 Image Smoothing/Filtering(Low pass):

OVER

2.Image Smoothing/Filtering(Low pass):

‐Does not work will on all the image [I = medfilt2(I, [1 5]);] •No effect most of the time•Absence of salt and peeper noise

‐Tendency of loosing interesting detailsTendency of loosing interesting details

Image segmentation K‐means Clustering


Class: Benign

g y g

mdb212 150 200mdb214 150 200 00

) (a)Original image

mdb214 150 200mdb218 150 210mdb219 150 210db222 150 210 in

tensity

>20

2

3

mdb222 150 210mdb223 150 210mdb226 150 210

sity <130) (i

1

(b) Segmentation Part

mdb227 150 210mdb236 150 210mdb240 150 210

(inten

mdb248 150 210mdb252 140 210

(C) Final Segmented Image



(e)Image containing

g y g

Achievement: X‐Ray Label removedClass: Benign

(b)Segmentation Part (c) Final Segmented Image(a)Main Image (e)Image containing Duct, Lobules, Sinus

(d)Binary Image

mdb236.jpg

mdb001.jpg

mdb254.jpg



Issues 1.Biggest Cluster Does Not Contain BreastProduce Artifacts In Pectoral muscle And Breast Region

Class: Benign(b)Segmentation Part (c) Final Segmented Image(a)Main Image (e)Image containing

only Pectoral muscle(d)Binary Image

Class: Benign

mdb212 jpg

What we need

mdb212.jpg

What we have



( )I C i i

g y g


Class: Benign(b)Segmentation Part (c) Final Segmented Image(a)Main Image

(e)Image Containing Only Pectoral muscle(d)Binary Image

Class: Benign

mdb214.jpg

mdb001.jpg

mdb218.jpg

What We Need What We Have



(e)Image containing duct, lobules,

g y g

(d)Binary Image


Class: Benign

(b)Segmentation Part (c) Final Segmented Image(a)Main Image(

sinus & Pectoral muscle(d)Binary Image

mdb222.jpg

ntW

mdb001.jpg

What w

e wa hat w

e Hav

mdb223jpg Wve

mdb226jpg



(e)Image containing duct lobules


Class: Benign

(b)Segmentation Part (c) Final Segmented Image(a)Main Image (d)Binary Image(e)Image containing duct, lobules,

sinus & Pectoral muscle

mdb240.jpg

mdb001.jpg

mdb248 jpg at we want W

hat we H

mdb248.jpg

Wha

Have

mdb252.jpg



Class: Malignant

g y g

mdb209 140 210mdb211 140 210 10

) (a)Original imagemdb209.jpg

(a) Original imagemdb213 140 210mdb216 140 210mdb231 140 210

intensity

>21

1

23

(a) Original image

mdb233 140 210mdb238 140 210mdb239 140 210

sity <140) (i 1

(b)Segmentation Part

mdb241 140 210mdb245 140 210mdb249 140 210db253 140 210(in

ten

mdb253 140 210mdb254 140 210mdb256 140 210 (C) Final Segmented Image



Class: Malignant

Achievement: X‐Ray Label removed

(b)Segmentation Part (c) Final Segmented Image(a)Main Image(e)Image containing duct, lobules, sinus(d)Binary Image

Class: Malignant

mdb238.jpg

el Rem

oved

mdb241.jpg Labe

mdb245.jpg



Issues Level Remain In The ImageProduce Artifacts In Pectoral muscle And Breast Region

Class: MalignantClass: Malignant

mdb212 jpgmdb209.jpg

(e)Image Containing

(a)Main Image

mdb212.jpg Containingonly label

(b)Segmentation Part (d)Binary Image

(c) Final Segmented Image



Issues Pectoral muscle Remain In The ImageProduce Artifacts In Breast Region

Class: MalignantClass: Malignant

(b)Segmentation Part (c) Final Segmented Image(a)Main Image(e)Image containing only pectoral muscle(d)Binary Image


(b)Segmentation Part (c) Final Segmented Image(a)Main Image(e)Image containing only pectoral muscle(d)Binary Image

mdb213.jpg

mdb216.jpg



Expected output: PECTORAL muscle, DUCT, LOBULES, SINUS,LIGAMENTS

mdb256 jpg

Output ImageMain image

mdb256.jpg



Issues

Missing part Main image Image containing Output Image

Fatty tissue area,ligaments

Duct, Lobules, Sinus

Fatty tissue area,D L b l Si

X‐ray Labels

mdb001.jpg

Duct, Lobules, Sinus, ligaments

mdb209.jpg

Fatty tissue area,Duct, Lobules, Sinus,

ligaments

Pectoral muscle

jpg

mdb213.jpg



Challenge

g y g

1.Need A Non‐supervised Method

Find the binary threshold values.db212 150 200

No pre‐defined threshold value

(intensity <150)

mdb212 150 200

mdb214 150 200

mdb218 150 210

mdb219 150 210

db222 150 210 (intensity <150) (intensity >200)

mdb222 150 210

mdb223 150 210

mdb226 150 210

mdb227 150 210

db236 150 210

2. Segmentation Part

mdb236 150 210

mdb240 150 210

mdb248 150 210

mdb252 140 210

(K-means clustering)



2.Keeping fatty tissues and ligamentsChallenge

mdb001.jpg

mdb238.jpg

mdb212.jpg

jpg

jpg

mdb209.jpg

(a)Main Image (b)Result ImageFigure: Internal breast structure

mdb223jpg

(a)Main Image (b)Result Image

g ) g

X-ray Label Removing Finding The Big BLOB

The types of noise :High Intensity Rectangular LabelLow Intensity LabelLow Intensity LabelTape Artifacts

X-ray Label Removing Finding The Big BLOBPl f A ti

1.Binarizatin of original image.Plan of Action:

img=im2bw(img);(threshold luminance level‐=0.5)

2.Find the biggest blob.

( )

function [outim] = bwlargestblob( im,connectivity)if size(im,3)>1,error('bwlargestblob accepts only 2 dimensional images');

end

[imlabel totalLabels] = bwlabel(im,connectivity);sizeBlob = zeros(1,totalLabels);for i=1:totalLabels,

bl b( ) l h(f d( l b l ))sizeblob(i) = length(find(imlabel==i));end[maxno largestBlobNo] = max(sizeblob);

( ( ) ' ')outim = zeros(size(im),'uint8');outim(find(imlabel==largestBlobNo)) = 1;

end

X-ray Label Removing Finding The Big BLOBPl f A ti

1.Binarizatin of original image.Plan of Action:

(threshold luminance level‐=0.5)

Original image Binary Image

2.Find the biggest blob.

( )

mdb219.jpg

(a) Artifacts (Hole) in ROI

Original image Binary Image

Label successfully removed Issues

y g

(b)Absence of Ligaments and fatty tissue

mdb231.jpgmdb253.jpg

(c) Absence of pectoral muscles(c) Absence of pectoral muscles

X-ray Label Removing Finding The Big BLOBClass: Benign Issue with fatty tissues and ligaments existenceOriginal image Binary Image

(threshold luminance level‐=0.5) Original image Binary Image(threshold luminance level‐=0.5)

db212 j mdb219.jpgmdb212.jpg mdb219.jpg


mdb218.jpg mdb223.jpg

X-ray Label Removing Finding The Big BLOBClass: Benign Issue with fatty tissues and ligaments existenceOriginal image Binary Image



mdb227 jpg mdb248 jpgmdb227.jpg mdb248.jpg


X-ray Label Removing Finding The Big BLOBIssue with fatty tissues and ligaments existenceClass: Malignant

Original imageBinary Image





X-ray Label Removing Finding The Big BLOBIssue with fatty tissues and ligaments existenceClass: Malignant

Original image Binary Image(threshold luminance level‐=0.5) Original image Binary Image

(threshold luminance level‐=0.5)

mdb245.jpg

mdb238.jpg

mdb249.jpg

mdb239.jpg

mdb253.jpg


X-ray Label Removing

Issue With Fatty Tissues And Ligaments Existence

Moving towards solution

y g

X-ray Label RemovingPl f A tiPlan of Action:

1.Binarize the image

2.Fill inside the hole region of the binary image

3.Finding the largest Blob:function [outim] = bwlargestblob( im,connectivity)if size(im,3)>1,error('bwlargestblob accepts only 2 dimensional images');dend

[imlabel totalLabels] = bwlabel(im,connectivity);sizeBlob = zeros(1,totalLabels);for i=1:totalLabels,sizeblob(i) = length(find(imlabel==i));sizeblob(i) = length(find(imlabel==i));

end[maxno largestBlobNo] = max(sizeblob);

outim = zeros(size(im),'uint8');outim(find(imlabel==largestBlobNo)) = 1;

4.Keep the Largest Blob and discard other blobs(to remove X-ray level)

Image MorphologyGoal: Region filling(Region inside the blob)


Experimental results:

g f g( g )

Original imageFinding biggest blob(Level removed)

Hole fillingInside the blob(dialation)

Result image(Label Removed)Binary image

Direct Binarisation Without Image enhancement

g g (Level removed) Inside the blob(dialation) ( )y g

mdb240.jpg

mdb219.jpg

mdb231.jpg

Image Morphology X-ray Label RemovingGoal: Region filling(Region inside the blob)g f g( g )

Original imageResult image

(Label Removed)Binary image

Direct Binarisation Without Image enhancement Experimental results:

g g ( )y g

mdb240.jpg

Issues

1.Does not always produce appealing output

mdb219.jpgappealing output

2.Some details are missing(Details around Edge region )

mdb231.jpg

Image Morphology X-ray Label RemovingGoal: Region filling(Region inside the blob)g f g( g )

Original image Result image (Label Removed)

Direct Binarisation Without Image enhancement Experimental results:

g g

mdb240.jpg

mdb219 jpg

Issues

1 Does not always produce

mdb212.jpg

mdb219.jpg 1.Does not always produce appealing output

2.Some details are missing

mdb214.jpg

mdb231.jpg

(Details around Edge region )mdb219.jpg

mdb226.jpg


To Achieve The Desired Final Result:

-ApplyA Range Of Techniques on original image

-To find largest blobUse -Otsu’s thresholding technique (graytrash) [9]

-Finding Bi-level the image(im2bw)

[9] 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.

X-ray Label Removing Plan of Action

1. Histogram equalization of the original X-ray image2 Adjust image contrast2. Adjust image contrast3. Apply Otsu's Thresholding Method [9] and

fi d bi l l th i hi h h l bl b i itfind bi-level the image which has several blobs in it. 4. Finding the Largest blob (Bwlargest.bolb)5. Hole filling within the blob region6. Keep the true pixel value covering only the area of largest

blob and discard other features from the original image7. X-ray label is successfully removed

[9] 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.

X-ray Label Removing Combining Range of techniquesJ = histeq(I); %histogram equalizationJ = histeq(I); %histogram equalization

contrast_image = imadjust(J, stretchlim(J), [0 1]); %high contrast image

%Apply Thresholding to the Image level = graythresh(contrast_image);

mdb239.jpg

%GRAYTHRESH Global image threshold using %Otsu's methodbw_image = im2bw(contrast_image, level);%getting binary image

1.Original image

2.HistogramgEqualization

3.Contrast Image

4.Binary Image

X-ray Label Removing Combining Range of techniques

7.Result image(Label Removed)

6.Hole fillingInside the blob

5.Finding biggest blob

X-ray Label RemovingCompare the original and final image

Result image(Label Removed)

Original image


E l lExperimental results

X-ray Label Removing2.Histogram 6.Hole filling 7.Result image

Benign1.Original image

gEqualization 3.Contrast Image 4.Binary Image 5.Finding biggest blob

gInside the blob

g(Label Removed)

mdb212.jpg

mdb214.jpg

mdb214.jpg

mdb218.jpg

mdb219.jpg

X-ray Label Removing Benign2.Histogram 6.Hole filling 7.Result image

1.Original imageg

Equalization 3.Contrast Image 4.Binary Image 5.Finding biggest blobg

Inside the blobg

(Label Removed)

mdb222.jpg

mdb223.jpg

mdb226jpg

mdb227jpg

X-ray Label Removing Benign2.Histogram 6.Hole filling 7.Result image

1.Original imageg


Inside the blobg

(Label Removed)

mdb226.jpg

mdb240.jpg

mdb248.jpg

mdb252.jpg

X-ray Label Removing Malignant2.Histogram 6.Hole filling 7.Result image

1.Original imageg


Inside the blobg

(Label Removed)


mdb211.jpgjpg

mdb213.jpg

mdb216.jpg


1.Original imageg


Inside the blobg

(Label Removed)

mdb233.jpg


mdb239.jpg

mdb241.jpg


1.Original imageg


Inside the blobg

(Label Removed)

mdb245.jpg

mdb249.jpg

mdb253.jpg

mdb256.jpg

Finally!

X-ray Label RemovingSuccessful

X ray Label Removing

mdb212.jpg(a)Main Image (b)Result Image

mdb213.jpg(a)Main Image (b)Pectoral Muscle

Removing pectoral muscleKeeping fatty tissues and ligaments

mdb214.jpg

Main ImageMain Image

Result Image

Extraction of ROIRemoving pectoral muscle

Why removing pectoral muscle?

o Pectoral muscle will never contain micro‐calcification

o Less Computational Time And Costo Less Computational Time And Cost‐Operation on small image area

o Fat ty t i s sue areao D u c to Fat ty t i s sue areao D u c t

Existence of micro‐calcification:

o D u c to Lobule so Sinus

l i t

o D u c to Lobule so Sinus

l i to l i g a m e nt s o l i g a m e nt s ROI

Edge Detection of pectoral muscleRemoving pectoral muscle

P ibl A h T Ed d iPossible Approach To Edge‐detection:

1.Scanning pixel value intensity at each points2.find out the sudden big intensity change at the edge location

Approach‐01:

3.Mark the pixels at edge location4.Estimate a straight line depending on the marked edge points

Problem faced in Approach‐01:

‐Finding appropriate Thresholding value in an unsupervised method, which will work on every image ‐The threshold value must be found in an unsupervised mannerA d fi d h h ld l ill d d i d f ll i‐Any predefined threshold value will not produce desired output for all image

mdb212 150 200mdb214 130 205db218 150 210mdb218 150 210

mdb219 120 200mdb222 150 210mdb223 150 225mdb226 110 210mdb227 150 230mdb236 160 210mdb240 150 200mdb248 150 210mdb252 140 210


Possible Approach To Edge‐detection:

Approach‐02:

1.Segment the image

Approach 02:

2.Separate the pectoral muscle form the Duct, Lobules, Sinus region Making all the pixels black(zero)resides in the fatty tissue and ligament area

3.Find the binary image of image found in step 2(it will be used as outer image)

4.Erode the image found in step‐3 (it will be used as inner image)g p ( g )

5.Subsract the inner image from the outer image to get the edge

Visualization in next slide


Possible Approach To Edge‐detection(Approach‐02):Possible Approach To Edge detection(Approach 02):

1.Original imagemdb212.jpg

2.Segmentation Part

3.Fatty tissue& Ligament removed


Possible Approach To Edge‐detection(Approach‐02):Possible Approach To Edge detection(Approach 02):

6.Edge(outer‐inner)

4.Binary Version(outer)

5.Binary Version(inner)

y ( )


Possible Approach To Edge detection(Approach 02):Possible Approach To Edge‐detection(Approach‐02):



1 Original image 2 Segmentation Part 4 Binary Version(outer)

3.Fatty tissue& Ligament removed 5.Binary Version(inner) 6 Edge(outer‐inner)1.Original image 2.Segmentation Part 4.Binary Version(outer)g 6.Edge(outer inner)

mdb212.jpg

mdb214.jpg

mdb218.jpg

mdb252.jpg


1 Original image 2 Segmentation Part3.Fatty tissue

& Ligament removed4 Binary Version(outer)5.Binary Version(inner) 6 Edge(outer‐inner)1.Original image 2.Segmentation Part g 4.Binary Version(outer) 6.Edge(outer inner)

mdb223.jpg

mdb226.jpg

mdb240.jpg

mdb248.jpg


P bl f d ( h )

1.Pectoral muscle and ligaments in fatty tissue area got merged

Problems faced in (Approach‐02):

1.Pectoral muscle and ligaments in fatty tissue area got merged

1.Original image 2.Segmentation Part3.Fatty tissue

& Ligament removed4.Binary Version(outer)5.Binary Version(inner) 6.Edge(outer‐inner)

mdb218.jpg

mdb240.jpg


P bl f d ( h )

2.Discontinuity in Pectoral muscle edge



& Ligament removed4.Binary Version(outer)5.Binary Version(inner) 6.Edge(outer‐inner)

mdb226.jpg

mdb248 jpg


mdb248.jpg


P bl f d ( h )Problems faced in (Approach‐02):

3 S h h ldi l (i 130 210 ) d k ll ll h i d3.Same thresholding value(i.e.,130‐210,) does not work well on all the images and Produce improper output(complete black image as output)


& Ligament removed4.Binary Version(outer)

5.Binary Version(inner)6.Edge(outer‐inner)


Points to be noted from approach-2:(2)Binary Image(1)Original Image

‐Pectoral muscle a Triangular areamdb212.jpg

Based on this point: M i t h 03

mdb214.jpg

Moving on to approach ‐03

mdb209.jpg

Triangle Detection of pectoral muscleRemoving pectoral muscle

1 Fi h i l f h l l i

Approach‐03(Triangle Detection of pectoral muscle):

1.Fing 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 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 lineV. Draw a vertical line and angular line.

2.Making all the pixels black(zero)resides in the pectoral muscle area

Visualization in next slide


Approach 03(Triangle Detection of pectoral muscle):Approach‐03(Triangle Detection of pectoral muscle):stratching_in_range=uint8(imadjust(I,[0.01 0.7],[1 0]));

BW=~stratching_in_range;

mdb212.jpg1.Original image

2.Contrast stretching

3.Binary of contrast image


Approach 03(Triangle Detection of pectoral muscle):Approach‐03(Triangle Detection of pectoral muscle):

6.muscle removed

5.Triangle Filled

4.Triangle

Removing pectoral muscleApproach 03(Triangle Detection of pectoral muscle):

Triangle Detection of pectoral muscle

Approach‐03(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: Benign

g g .Co t ast st etc g y g g g

mdb212.jpg

mdb214.jpg

mdb240.jpg

mdb248.jpg


P bl f d ( h )

Class: Benign


The triangle does not always indicates the proper pectoral muscle area.

2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed

Reason: Discontinuity in edges (First 3 or 4 rows and columns)

mdb222.jpg

mdb226.jpg

mdb227.jpg


P bl f d ( h )

Class: Benign



1.Original image 2.Contrast stretching3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed


mdb218.jpg

mdb218.jpg

mdb219.jpg


P bl f d i (Approach 03)

Class: Benign



1.Original image 2.Contrast stretching3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed


mdb222.jpgjpg

mdb219.jpg



Class: Benign


Defects in mammogram

2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed

g

mdb223.jpg



Class: Benign

2.Contrast stretching1.Original imageProblems faced in (Approach‐03):

Defects in mammogram

3.Binary of contrast image 4.Triangle

mdb227.jpg

g

5.Triangle Filled 6 muscle removed5.Triangle Filled 6.muscle removed

Triangle Detection of pectoral muscleRemoving pectoral muscle Class: Malignant

1.Original image

2 Contrast stretching

6.muscle removedmdb256.pg

2.Contrast stretching

3.Binary of contrast image

4.Triangle

5.Triangle Filled


Problems faced in (Approach‐03): 1 In case of FLOOD FILLING:


Problems faced in (Approach‐03): 1.In case of FLOOD FILLING:Lickage in angular line cause false output

mdb209.jpg

Mdb245.jpg

mdb213jpg

mdb216.jpg


Problems faced in (Approach‐03): 2 Discontinuity in edge lines causes false output


Problems faced in (Approach‐03): 2.Discontinuity in edge lines causes false output

mdb231.jpg

mdb233jpg

mdb238jpg

mdb239jpg


Problems faced in (Approach‐03): 2 Discontinuity in edge lines causes false output


Problems faced in (Approach‐03): 2.Discontinuity in edge lines causes false output

mdb241jpg

mdb211.jpg

dbMdb249.jpg

Removing pectoral muscleP i b d f h 3

Triangle Detection of pectoral muscle

1. The triangle does not always indicates the proper pectoral muscle area.

Points to be noted from approach-3:

• Reason: Discontinuity in edges (First 3 of 4 rows and columns

mdb218.jpg

2. Defects in mammogram

3. The angular line is not a straight line(A slightly curve line)

Based on these points: Moving on towards ALTERNATIVE APPROACH

Alternative approachRemoving pectoral muscle

Overview of the proposed method

A MLO Mammogram


Extraction of Region of Interest

ROI Image

Watershed TransformationIdentified Pectoral Muscle

ROI Image

Gradient Operation

S thi Filt

Proposed Merging AlgorithmGradient Image

Smoothing Filter

Watershed Transformation

Filtered ImageOver‐segmented ImageWatershed Transformation

Alternative approachRemoving pectoral muscle

ROI(ABCDA)

Extraction of ROIRemoving pectoral muscleA BP lA BPectoral

muscle

Eliminate:‐Unexposed X‐ray portion (left side)‐top left most pixel is a pectoral muscle

Determine:Skin air boundary

hhRegion of Interest

‐Skin‐air boundary‐ Region of interest(ROI)

Result:Result:‐ROI(ABCDA) includes complete pectoral muscle

D C ROI(ABCDA)

Removing pectoral muscle Extraction of ROI

Image # mdb226.jpg1. ORIGINAL IMAGE 2.BINARY IMAGE

3 Blue Circle indicates the3. Blue Circle indicates the True ‘Skin‐air boundary’ in

the binary image

4. True ‘Skin‐air boundary

5. Extraction of ROI

Removing pectoral muscle Benign Extraction of ROI

1 Original image 2 Binary image 3 Blue Circle indicates the True ‘Skin‐air 4. True ‘Skin‐air boundary 5. Extraction of ROI

mdb212 jpg

1.Original image 2.Binary image 3. Blue Circle indicates the True Skin air boundary in the binary image

y

mdb212.jpg


db218 jmdb218.jpg



mdb223.jpg

Removing pectoral muscle Extraction of ROIBenign

3 Blue Circle indicates the True ‘Skin air 4 Tr e ‘Skin air bo ndar 5 E t ti f ROI1.Original image 2.Binary image3. Blue Circle indicates the True Skin‐air

boundary in the binary image4. True ‘Skin‐air boundary 5. Extraction of ROI

mdb226.jpg

mdb227.jpg

mdb240.jpg

mdb248.jpg

mdb252.jpg

Removing pectoral muscle Malignant Extraction of ROI

1 Original image 2 Binary image3. Blue Circle indicates the True ‘Skin‐air 4. True ‘Skin‐air boundary 5. Extraction of ROI

mdb209.jpg

1.Original image 2.Binary image boundary in the binary image

db 09.jpg

mdb211.jpgjpg

mdb213.jpgjpg

mdb216.jpg

mdb231.jpg

mdb233.jp


1 Original image 2 Binary image3. Blue Circle indicates the True ‘Skin‐air

4 True ‘Skin air boundary 5 Extraction of ROI

Malignant

1.Original image 2.Binary image boundary in the binary image 4. True Skin‐air boundary 5. Extraction of ROI

mdb238.jpg

mdb239.jpg

mdb241.jpg

mdb245.jpg

Removing pectoral muscle Extraction of ROIMalignant

1.Original image 2.Binary image3. Blue Circle indicates the True ‘Skin‐air

boundary in the binary image4. True ‘Skin‐air boundary 5. Extraction of ROI

mdb249.jpg

mdb253.jpg

mdb256.jpg


Extraction of ROISuccessful

Extraction of ROImdb212.jpg

mdb214.jpg

mdb222.jpg


Removing pectoral muscleOverview of the proposed method

A MLO Mammogram



ROI Image


ROI Image

Gradient Operation

S thi Filt

Proposed Merging AlgorithmGradient Image

Smoothing Filter


Filtered ImageOver‐segmented ImageWatershed Transformation

Removing pectoral muscle


Gradient OperationGradient Operation

Smoothing Filter

Gradient Image

Smoothing Filter


Filtered Image

Removing pectoral muscleGradient Operation SOBEL GRADIENTGradient Operation SOBEL GRADIENT

Extraction of ROI ROI Gradient

Removing pectoral muscle Gradient Operation

Gradient Operation SOBEL GRADIENTGradient Operation SOBEL GRADIENT

1.Getting Horizontal and Vertical Gradient:

%#Sobel mask for x‐direction:Gx=((C(i+2,j+2)+2*C(i+1,j+2)+C(i,j+2))‐(C(i+2,j)+2*C(i+1,j)+C(i,j)));%#Sobel mask for y‐direction:Gy=((C(i+2,j)+2*C(i+2,j+1)+C(i+2,j+2))‐(C(i,j)+2*C(i,j+1)+C(i,j+2)));

Define SOBEL MASK PAIR (Gx, Gy)to convolve with the original image

‐1 0 1 ‐1 ‐2 ‐1

‐2 0 2

‐1 0 1

0 0 0

1 2 1

(a) Gx=Sobel Horizontal Mask(To compute horizontal

(b)Gy=Sobel Vertical Mask(To compute

vertical gradient)gradient)

g )

(a)horizontal gradient =Original image*Gx (b)Vertical gradient = Original image*Gy

Figure‐2: A 3*3 Convolution musk(Sobel Musk) to obtain ROI Gradient

Removing pectoral muscleGradient Operation Original image CONVOLVED with SOBEL MUSK to get SOBEL GRADIENT

Gradient Operation

Gradient Operation Original image CONVOLVED with SOBEL MUSK to get SOBEL GRADIENT

2.The COMBINATION of HORIZONTAL AND VERTICAL GRADIENT renders :THE GRADIENT MAGNITUDE.THE GRADIENT MAGNITUDE.

Gradient Magnitude(SOBEL ROI): Gradient direction:

G= Gx2+Gy2 tan‐1 (Gy/Gx)

Sobel ROI

Removing pectoral muscleGradient Operation SOBEL GRADIENT ROI Gradient

Gradient Operation

Gradient Operation SOBEL GRADIENT, ROI GradientB(i,j)=sqrt(Gx.^2+Gy.^2);……………………………..%#mean filtering to smooth ROI gradient(B)kernel = ones(3, 3) / 9; %# 3x3 mean kernel

( )Filtered_ROI_Gradient = conv2(B, kernel, 'same'); % Convolve keeping size of I;Mean filtering is usually thought of as a convolution filter.

3.Smoothing SOBEL ROI(ROI Gradient):

Mean filter 3*3 is usedMean filter :

Smoothes Local VariationReduce Noise

The FILTERED image is called ROI Gradient.

ROI Gradient image is used as input toROI Gradient image is used as input to The Watershed Transform.

(d)Sobel ROI (d)Filtered ROI Gradient


Gradient Operation

%#The gradient of the image/magnitude%#B(i,j)=abs(Gx)+abs(Gy); %#To avoid complex computation, the gradient can also be computed using

this formulaB(i,j)=sqrt(Gx.^2+Gy.^2);

Gradient Operation SOBEL GRADIENT, ROI Gradient

%#to find x‐direction derivative(Vertical Direction)Bx(i,j)=sqrt(Gx.^2);

%#to find y‐direction derivative(Horizontal Direction)By(i,j)=sqrt(Gy.^2);

l Gradien

tmdb212.jpg(a)Original Image

radien

t

ent(b+

c)(d)Filtered ROI

Gradient

(b)Horizon

ta

(c)Vertical Gr

(d)ROI G

radie Gradient


Gradient Operation


(b)Horizontal Gradient

mdb212.jpg.

(a)Original Image (c)Vertical Gradient (d)ROI Gradient(b+c) (d)Filtered ROI Gradient


Gradient Operation




Benign Gradient Operation


1.Original image 2. Extraction of ROI (b)Horizontal Gradient (c)Vertical Gradient (d)ROI Gradient(b+c) (d)Filtered ROI Gradient

mdb212.jpg

db214 jmdb214.jpg

mdb218.jpg

mdb219.jpg




1.Original image 2. Extraction of ROI (b)Horizontal Gradient (c)Vertical Gradient (d)ROI Gradient(b+c)(d)Filtered ROI

Gradient

mdb222.jpg

mdb223.jpg

mdb226.jpg

mdb227.jpg





Gradientg g 2. Extraction of ROI ( ) ( ) ( ) ( )

mdb240.jpg

mdb248.jpg



Malignant Gradient Operation



Gradient

mdb209.jpg


mdb213.jpg

mdb216.jpg





Gradient

mdb231.jpg

mdb233.jpg

mdb238.jpg

mdb239.jpg





Gradient

mdb241.jpg

mdb245.jpg

mdb249.jpg

mdb253.jpg




1.Original image

2 Extraction of ROI2. Extraction of ROI

3.Horizontal Gradient

4.Vertical Gradient

5.ROI Gradient(b+c)

6.Filtered ROI Gradient





Gradient Operation

S thi Filt

Gradient Image

Smoothing Filter


Filtered Image


Removing pectoral muscle WatershedTransformation


Th t h d t f fi t d b Di b l d L t j l ([10]The watershed transform was first proposed by Diagabel and Lantuejoul.([10] A region‐based segmentation approach from the field of mathematical morphology, and a well‐organized survey of its different definitions and algorithms can be found in the work of Roerdink and Meijster[11]j [ ]

The concept of watershed transform can be realized by visualizing the ROI gradient as a topographic surface, such that the gray value of each pixel defines its altitude.

A hole is pierced in each regional minimum which allows water to gradually rise in catchment basins;

Each basin is evolved from a regional minimum.

When any two catchment basins are about to merge, a dam is built between them to prevent them from mergingprevent them from merging.

When water reaches the highest peak of the landscape, the flooding process is stopped.

Finally, several catchment basins divided by dams (otherwise called watersheds) are evolved. In terms of image segmentation, these catchment basins represent different regions, and watersheds are the boundaries between these regions.


1 Filtered ROI Gradient

2.Watershed linesobtained from image SOBEL_gradient (wr)


3 internal markers3.internal markers(light grayshed regions inside catchment basins)

4.External markers(background markers‐‐watershed lines)


5.Modified gradient imageobtained from internal and external markers

6.Watershed lines obtained from image SOBEL gradient (Lrgb)

7.Regional minima superimposed on original image


8.Modified regional minima superimposed on original image9.Binay of Opening


Experimental resultsp


2Watershed1 Filtered ROI 3 internal 4 External 5.Modified 6Watershed7.Regional 8.Modified

9 Bi f

Malignant

2.Watershed lines


3.internal markers

4.External markers gradient

image

6.Watershed lines

minima superimposed on original image

regional minima superimposed on original image

9.Binay ofOpening

mdb209.jpg


mdb213.jpg

mdb216.jpg



9 Bi f

Malignant

2.Watershed lines


3.internal markers


image

6.Watershed lines



9.Binay ofOpening

mdb231.jpg


mdb238.jpg

mdb239.jpg



9 Bi f

Malignant

2.Watershed lines


3.internal markers


image

6.Watershed lines



9.Binay ofOpening

mdb241.jpg

mdb245.jpgjpg

mdb249.jpg

mdb253.jpg


Problems faced:

Irrelevant output:pDoes not indicate the pectoral muscle appropriately on all mammograms

7 Regional 8 M difi d2.Watershed

lines1.Filtered ROI

Gradient3.internal markers

4.External markers

5.Modified gradient image

6.Watershed lines

7.Regional minima

superimposed on original image

8.Modified regional minima superimposed on original image

9.Binay ofOpening

mdb238.jpg

mdb253.jpg


Alternative Approach:

U M i Al ith t th C t h tUse Merging Algorithm to merge the Catchment basins resides in Pectoral muscle areas

mdb238.jpg1.Filtered ROI

6.Watershed lines

2.Watershed lines

Gradientlines

Over‐segmented Imagelines

3.internal markers 4.External

markers

5.Modified gradient image

Removing pectoral muscleOverview of the proposed method

A MLO Mammogram



ROI Image


ROI Image

Gradient Operation

S thi Filt

Propose a Merging AlgorithmGradient Image

Smoothing Filter


Filtered ImageOver‐segmented Image


Future plan

Our proposed method consists of three main modules:) i f l f1) Basic of wavelet transform

2) Micro‐calcifications detection 3) Evaluation of detection methods)

1 Basic of wavelet transform:

Future plan1.Basic of wavelet transform:

-Wavelet analysis is an extremely powerful data representation method

-Allows :The separation of images into frequency bands without affecting the spatiale sep o o ges o eque cy b ds w ou ec g e splocality Bouyahia et al. [12].

-Makes use of :Makes use of :two separate bases for analysis and synthesis.

Information Extraction:-Information Extraction:localized high frequency signals such as micro-calcifications could be extracted

Th t di i l l t t f ill b hi d-The two dimensional wavelet transform will be achieved:By implementing a bank of one-dimensional low-pass and high-pass analysis filters.

1 wavelet transform:

Future plan1.wavelet transform:

For one level of decomposition:-For one level of decomposition:The image will be decomposed into four orthogonal sub-bands:LL, HL, LH, and HH.

-The four "detail" images:

1) Low-High (LH)2) High-Low (HL)3) High-High (HH)3) High-High (HH)4) Low-Low(LL)

ill d di i f b dwill correspond to distinct frequency bands.

1 B i f l t t f


The HL sub band will contain :-The HL sub-band will contain :Horizontal oriented features.

-The LH sub-band will contain:Vertically oriented structures

-The HH sub-band will contains :Diagonal structures.

-The LL sub-band will be :The low-pass filtered version of the imageThe low pass filtered version of the image

and will further be decomposed

1 Basic of wavelet transform:


-Collection of sub-images will form:A multi resolution representationp

Multi resolution representation will organize:-Multi resolution representation will organize:The image into a set of details appearing

at different resolutions.

2 Micro calcifications detection:

Future plan2. Micro‐calcifications detection:‐Full resolution will be maintained

during the multi‐resolution analysis by using wavelet transform.

‐The wavelet transform will be operated :without down‐sampling and up‐sampling in respectively the analysis and synthesis

computations.computations.

‐This will ensure:translation invariance and

implies –a finer sampling rate of the wavelet decompositiona vital requirement during small object detection such as micro‐calcifications.

‐The redundant transform will be applied:In each pixel of the image.

The size of each sub‐band will be:The same as the original image.

2 Micro calcifications detection:

Future plan2. Micro‐calcifications detection:

Three levels redundant wavelet decomposition of the image will be performed with bi‐orthogonal daubechies wavelet , daubechies et al. [13]

The wavelet decomposition is performed after an enhancement Step Bouyahia et al. [12]

First level detail coefficients will contain mostly noise.First level detail coefficients will contain mostly noise.

Detail coefficients in level 2 and 3 will contain fine breast structure and micro‐calcifications.

Af d i i f h i h l f b b d ill b ( hAfter decomposition of the image, the low‐frequency sub‐band will be set to zero (the micro‐calcifications will appear in the high‐frequency sub‐bands).

An adaptive thresholding will be performed to detect micro‐calcifications. p g p

After wavelet Decomposition, we will determine the maximum value in each sub‐band.

We will threshold the detail coefficients of each sub band with the corresponding thresholdWe will threshold the detail coefficients of each sub‐band with the corresponding threshold and perform the reconstruction of the image.

The process will be iterated by varying the thresholds with logarithmic way.

Future plan3) Evaluation of detection:

Simulations will be operated on Mini‐Mammographic Image Analysis Society (MIAS) database

The results will be presented and compared to some relative works.

We will show that the proposed approach will competitive with the best of the state of the artthe best of the state of the art.

REFERENCES

[1]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

[2]D.Narain Ponraj, M.Evangelin Jenifer, P. Poongodi, J.Samuel Manoharan “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer”, Journal ofEmerging Trends in Computing and Information Sciences, Volume 2, Issue 12, pp. 656‐664

[3]Arnau Oliver, Albert Torrent, Meritxell Tortajada, Xavier Llad´o,Marta Peracaula,d d l ´ d d ” l f d lLidia Tortajada, Melcior Sent´ıs, and Jordi Freixenet,” Automatic microcalcification and clusterdetection for digital and digitised mammograms”, Springer‐Verlag Berlin Heidelberg, 36,pp. 251–258, 2010

REFERENCES

[4]Arnau Olivera, Albert Torrenta , Xavier Lladóa , Meritxell Tortajada, Lidia Tortajadab,Melcior Sentísb, Jordi Freixeneta, Reyer Zwiggelaarc,” Automatic microcalcification and clusterdetection for digital and digitised mammograms”,Elsevier:Knowledge‐Based Systems, Volume 28,pp. 68–75, April 2012.

[5]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

[6]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

[7]M.Rizzi, M.D’Aloia, B.Castagnolo,” Computer aided detection of microcalcification in digitalM d i l d i i ” I C Aid d E V l 16 I 2Mammograms adopting a wavelet decomposition ”,Integr.Comput.‐Aided Eng.,Vol 16,Issue 2,pp.91‐103,2009

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