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

1. 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

2. 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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3. 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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4. 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)

5. 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)

6. 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)

7. 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)

8. SRGE Workshop, Cairo University (7-November-2015)
Proposed Approach
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Preprocessing
Phase
Breast Region of
Interest
Segmentation
Features
Extraction
Statistical
Features
Texture
Features
Gabor features
SIFT features
SURF Features
Features
Reduction
Mutual
Information
Statistical
Dependency
Random Subset
Feature
Sequential
Floating
Forward
Sequential
Forward
Principle
Component
Analysis
Genetic Algorithm
Grey Wolf
Optimizer
Classification
Support Vector
Machine

9. Results and Discussion
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Dataset
A 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)

10. Results and Discussion
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Grey Wolf Parameters Setting
Parameter Value(s)
Number of Features 127
Number of Search Agents (Wolves) 200
Number of Iterations 5
Range (Boundary of Search Space) [1 127]
Dimension 127
Fitness Classification
Accuracy
SRGE Workshop, Cairo University (7-November-2015)

11. Results and Discussion
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Comparison Between Different No. of
Extracted
Features From SIFT in Terms of
AccuracyNo. of
Features
Accurac
y (%)
Kernel
Function
10 56.10 RBF
30 60.98 RBF
70 51.22 Quadratic
110 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
Accurac
y
Kernel
Function
10 51.22 Quadratic
30 58.54 Quadratic
70 58.21 Quadratic
110 52.62 RBF

12. Results and Discussion
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SRGE Workshop, Cairo University (7-November-2015)

13. Results and Discussion
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Statistical Features
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SRGE Workshop, Cairo University (7-November-2015)

14. Results and Discussion
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Quadratic Polynomial RBF Linear
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15. Results and Discussion
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Comparison between proposed approach and other approaches
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SRGE Workshop, Cairo University (7-November-2015)

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

17. Thanks and Acknowledgement
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SRGE Workshop, Cairo University (7-November-2015)