Group Members• Shahrin Ahammad Shetu• Farzin Raeeda Jamil • Md.A.Mannan Joadder• Mohd.Ashique Ridwan Nayeem

• Supervisor: Abdullah Al Helal Department of EEE United International University

OutlineIntroductionWork-FlowFeature ExtractionFeature SelectionClassificationExperiment & ResultsConclusion

What is cancer ?Abnormal growth of cells

Affects major body parts

Results in degradation of the body and eventually death

Second most major cause of death

Breast CancerSecond most lethal form of cancer in women

Large number are affected and die every year

Early detection helps fight cancer more effectively

Lung & Bronchus

26%

Breast CancerBreast Cancer

15%15%

Colon & Rectum

9%

Pancreas

7%

Ovarian Cancer

6%

MammographyMammography is the most common detection technique

But uses ionizing radiationExpensiveWorks poorly in dense regions (in young women)

Ultrasonography

Alternative to mammography

CheapNon-invasiveHarmless

Challenges of Ultrasonography

It requires interpretation by experts.

Thus it is operator dependent.High inter-observer variation rate

CAD using ultrasonogramsComputer Aided Diagnosis (CAD) tool may be used to detect malignant tumor.

CAD is relatively operator independent.

Reduces inter-observer variation rate

Should have a high accuracy

CAD Steps

ObjectiveOur aim was to improve CAD tool using feature selection method.

Feature Selection and Feature Extraction are integral parts of CAD system.

Feature selection is a relatively

unexplored region.

Large number of features will in aid

feature selection.

Our Focus

Initial StepsUltrasound scansPerform preprocessing like noise eliminationSegmentation into different ROIDr. Kaisar Alam from Riverside Research shared his ultrasound database of segmented images with us.

504 ultrasound scans (102 different patients)

50 were malignant, and the rest were benign.

MotivationResearchers proposed different types of features

No one really extracted a large number of features

Large number of features will assist feature selection

We considered different proposed features

We extracted a large pool of 58 features

FeaturesBenign and malignant tumor have different features.

A good feature has small difference for the same class, but large difference for two different classes.

Benign tumor is oval shaped, smooth textured.

Malignant tumors have irregular shape, rough texture etc

Benign Tumor

Malignant tumor

Features are extracted from segmented ultrasound scans.

Three major categories

Categories of Features

Morphological FeaturesDescribes tumor shape, boundary etc.Only the segment containing the lesion is considered.

Malignant lesions have poorly defined margins or boundaries.

Benign tumors have well defined margins.Malignant lesions are generally irregular in shape.

Benign lesions have regular or oval shape.

Examples PerimeterRepresents lesion perimeter

Malignant tumors have relatively larger perimeter due to irregular boundary

Thus larger value roughly indicates malignant tumor

AreaRepresents lesion area

Malignant lesions have larger areas

Due to regular shape benign tumors have smaller area

Thus smaller area indicates benign tumor

Acoustic Features EchogenicityAbility to bounce an echo

Benign tumors are more echogenic compared to malignant tumors.

HeterogeneityDefines uniformity in a substance

Benign lesions are less heterogenic compared to malignant lesions.

Texture FeaturesBased on textural propertyConsiders the entire ultrasound scansIt can detect variation of pixel intensities

Can detect texture smoothness Malignant tumors generally have larger variance of intensities

ExampleVariance contrast• Ratio of variance of the inside and the outside of the tumor

What is Feature Selection ?

A method by which useful features are

selected

It reduces the number of irrelevant

features

Also decreases the computational cost

Feature Selection

Feature Selection Techniques

Wrapper methodsFilter methodsWe have used four techniques.

Wrapper

Wrapper method considers that all combination of features are tested

Thus has a high computational cost.Hence only forward and backward feature selection are considered

Laplacian score

Filter methodWorks in both supervised and unsupervised condition

Main concept is when two data are close to each other they refer to the same object

Supervised and Unsupervised Learning

Functions from labeled training dataHidden structure in unlabeled training data

MCFS

Multi-cluster feature selectionRecently developed filter methodWorks for both unsupervised and supervised learning

Measures correlation between different features without label information

Performs better clustering and classification

Elastic Net

Embedded methodMix of ridge and lassoFeature shrinking and selection

Why Feature Selection ?A gigantic feature set can cause a

high computational cost

Feature selection excludes irrelevant

features

Previously unexplored in cancer

detection from ultrasonograms

What is Classification ?The method by which test tumor can be identified as benign or malignant

It works based on the features returned from the selection method

Sparse Representation Classifier (SRC)We employed sparse representation classifier (SRC)

This is a newly developed classifier SRC produced promising result in face recognition technique

Performance metric - Area Under The ROC Curve (AUC)Ideal : AUC = 100%

Worst: AUC = 50%

The higher AUC, the

better

Data Set504 pathologically proven ultrasound scans

454 benign tumors50 malignant tumors

Segmented into 9 ROIs

Feature ExtractionThree different types of features

A large number of features are

needed for efficient feature

selection

A total of 58 different features was

extracted.

Feature Extraction (contd.)Classification was carried out using all the features

AUC = 87.52% AUC of87.52%

WrapperA subset of features was obtained

This subset contained features from the original feature pool

AUC = 88.09%

AUC of 88.09%

Feature Selection Techniques

Laplacian Score

A subset of features was obtained

This subset contained 48 features from the original features

AUC = 90.37%

AUC of90.37%

MCFSMCFS produced the most promising feature subset

An optimized subset of 25 features was obtained

AUC = 93.31%

AUC of 93.31%

Elastic NetA subset of features was obtained

This subset contained 17 features from the original feature pool

AUC = 89.91%

AUC of 89.91%

We generated a large pool of 58 features that describe breast cancer.

An optimized feature subset using feature selection technique was obtained.

MCFS produced the most promising AUC of 93.31%.

This work was recognized as a conference paper in 2014.

Wrapping Up

The consistency of this method could be further enhanced using an even wider feature pool and more sophisticated feature selection technique.

Automated feature learning technique could be employed.

Future Work

With the advancement of technology over a period of time we as human beings

have progressed enormously! We as engineers hope to use these technology and knowledge to try to fight for the

positive to humankind andmake this world a better place