Breast Thermograms Features Analysis based on Grey Wolf Optimizer

*Faculty of Computers and Information, Cairo University and SRGE member

*Gehad Ismail Sayed and Aboul Ella Hassanien

http://www.egyptscience.net

Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University

Overview Introduction

What is Thermography? How Thermal Imaging Works? Problem Definition Motivation

Related Work Proposed Approaches Results and Discussion Conclusion and Future Works

SRGE Workshop, Cairo University (7-November-2015)

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Introduction

What is Thermography? Infrared Thermography is the science of

acquisition and analysis of thermal information from non-contact thermal imaging devices.

Thermography is non invasive functional imaging method, harmless, passive, fast, low cost and sensitive method.

SRGE Workshop, Cairo University Conference Hall (19-September-2015)

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Introduction

How Thermal Imaging Works? All objects emit infrared energy (heat) as a function of

their temperature. The infrared emitted by an object is known as its heat

temperature, where the hotter an object is , the more radiation its emits

Thermal camera is essentially a heat sensor that is capable of detecting tiny differences in temperature

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SRGE Workshop, Cairo University (7-November-2015)

Introduction

Problem Definition Breast cancer is the most common cancer among

women in the world. One out of 8 women will get breast cancer.

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SRGE Workshop, Cairo University (7-November-2015)

Introduction

Motivation Mammogram is one of the most imaging technology for

diagnosing breast cancer. Although mammogram has recorded a high detection and

classification accuracy, it is difficult in imaging dense breast tissues, its performance is poor in younger women and harmful, it couldn’t detect breast tumor that less than 2 mm and it’s very difficult to detect cancer in early stage

IRT could be a good source of images to study and detect the cancer at the early stages.

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SRGE Workshop, Cairo University (7-November-2015)

Related Work

Several approaches for classification Breast thermograms to normal or abnormal have been proposes which can be categorized to : Asymmetric classification based on comparison between

the extracted features from left and right breast Asymmetric classification based on extracted feature

from whole region of interest

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SRGE Workshop, Cairo University (7-November-2015)

SRGE Workshop, Cairo University (7-November-2015)

Proposed Approach8

Preprocessing Phase

Breast Region of Interest Segmentation

Results and Discussion9

DatasetA benchmark database used to evaluate the proposed approach. This is a public database that are constructed by collecting the IR images from UFF University's Hospital and publicly published under the approval of the ethical committee where every patient should sign consent. 61 IR breast images with resolution (640 x 480 pixels) from this database were used in this paper (29 healthy and 32 malignant).

SRGE Workshop, Cairo University (7-November-2015)

Results and Discussion10

Grey Wolf Parameters SettingParameter Value(s)

Number of Features 127Number of Search Agents (Wolves) 200

Number of Iterations 5Range (Boundary of Search Space) [1 127]

Dimension 127Fitness Classification

AccuracySRGE Workshop, Cairo University (7-November-2015)

Results and Discussion11

Comparison Between Different No. of Extracted

Features From SIFT in Terms of AccuracyNo. of

Features

Accuracy (%)

Kernel Function

10 56.10 RBF30 60.98 RBF70 51.22 Quadratic110 48.78 RBF

SRGE Workshop, Cairo University (7-November-2015)

Comparison Between Different No. of Extracted

Features From SURF in Terms of AccuracyNo. of

Features

Accuracy

Kernel Function

10 51.22 Quadratic30 58.54 Quadratic70 58.21 Quadratic110 52.62 RBF

Results and Discussion12

Quadr

atic

Polyn

omial RBF

Linea

r0.00

20.0040.0060.0080.00

100.00

SIFT

Quadr

atic

Polyn

omial RBF

Linea

r0.00

20.0040.0060.0080.00

100.00

SURF

SRGE Workshop, Cairo University (7-November-2015)

Results and Discussion13

Quadr

atic

Polyn

omial RBF

Linea

r0.00

20.00

40.00

60.00

Statistical Features

0.0020.0040.0060.0080.00

100.00

Texture Features

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Results and Discussion14

Quadratic Polynomial RBF Linear0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

Gabor Wavelet Features

Results and Discussion15

Comparison between proposed approach and other approaches

MI SDRSF

SSF

SSF

FS PCA GA

GWO

All Fea

tures

0.00%20.00%40.00%60.00%80.00%

100.00%

RBFLinearQua-dratic

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Conclusion and Future Works

Conclusion Several features extracted from breast region of interest have

been analyzed. Moreover, new features selector technique has been proposed and compared with 6 well known features selectors techniques and one of evolutionary techniques.

The obtained results shows the robustness of the It obtains over the all almost 97% accuracy

Future Works We plan to increase the number of breast thermograms images

dataset to evaluate the performance of the proposed approach and try new version of swarm.

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Thanks and Acknowledgement17

SRGE Workshop, Cairo University (7-November-2015)