UNIVERSITI PUTRA MALAYSIA

SALEH ALI ALSHEHRI

FK 2011 71

ULTRA WIDEBAND TECHNIQUE FOR BREAST CANCER DETECTION USING MULTI-LAYER FEED-FORWARD NEURAL NETWORKS

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ULTRA WIDEBAND TECHNIQUE FOR BREAST CANCER DETECTION

USING MULTI-LAYER FEED-FORWARD NEURAL NETWORKS

By

SALEH ALI ALSHEHRI

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Fulfillment of the Requirements for the Degree of Doctor of Philosophy

June 2011

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DEDICATION

To my Parents,

My Wife,

My Sons and Daughters

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment

of the requirements for the degree of Doctor of Philosophy

ULTRA WIDEBAND TECHNIQUE FOR BREAST CANCER DETECTION

USING MULTI-LAYER FEED-FORWARD NEURAL NETWORKS

By

SALEH ALI ALSHEHRI

June 2011

Chairman : Assoc. Prof. Adznan Bin Jantan, PhD

Faculty : Engineering

Breast cancer is one of the main causes of women‘s death. Early detection of tumors

increases the chances of overcoming this disease. There are several diagnostic

methods for detecting tumors, each of which has its own limitations. Recently, Ultra

Wideband (UWB) imaging has gained wide acceptance for several good features

such as its specificity and lack of ionizing radiation. The confocal method has been

the dominant technique in this area based on homogeneous breast tissues and prior

knowledge of tissue permittivity. Hence it is impractical and difficult to be

implemented clinically.

This thesis has focused on development of a complete non-confocal system for

breast tumor detection using Neural Network (NN)-based Ultra Wideband (UWB)

imaging considering both homogeneous and heterogeneous tissues. The work has

been done in two phases: i) Simulation and ii) Experiment.

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At the simulation stage, a feed-forward NN model was developed to identify the

existence, size, and location of tumors in a breast model. Spherical tumors were

created and placed at arbitrary locations in a hemispherical breast model using the

Computer Simulation Technology (CST) software as an Electromagnetic (EM)

simulator. The UWB signals were transmitted and received through breast phantoms.

The transmitter and receiver were rotated 360° to detect tumor existence, size, and

location in a two-dimensional breast slice using the best-complement rule. A

modified Principle Feature Analysis (PFA) method was implemented to reduce the

feature vector size and extract the most informative features. We have found that the

most informative features occur at the maxima and minima of the signals. The

extracted features from the received UWB signals were fed into the NN model to

train, validate, and test it first and then to detect the presence, size, and location of

possible breast tumors.

After simulation proof, a system was developed for experimental tumor detection.

The system consisted of commercial UWB transceivers, a developed NN model, and

breast phantoms for homogenous and heterogeneous tissues. The breast phantoms

and tumor were constructed using available low cost materials and their mixtures

with minimal effort. The materials and their mixtures were chosen according to the

ratio of the dielectric properties of the breast tissues. A Discrete Cosine Transform

(DCT) of the received signals was used to construct the feature vector to train the

NN model. Finally, the system was trained to distinguish between malignant and

benign tumors.

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Tumors as small as 0.1 mm and 0.5 mm (diameter) have been successfully detected

through simulation and experimental investigation respectively. The tumor existence,

size, and location detection rate are about (i) 100%, 93%, and 93.3% and (ii) 100%,

95.8%, and 94.3% through simulation and experimental system respectively.

Possible differentiation between malignant and benign tumor was also achieved. The

method utilizes the power of neural networks and demonstrates a new direction in

this field. This gives assurance of breast tumor detection and the practical usefulness

of the developed system in the near future.

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Abstrak tesis yang dikemukakan kepada Senat Unversiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

TEKNIK JALUR ULTRA LEBAR (UWB) UNTUK

PENGESANAN KANSER PAYUDARA MENGGUNAKAN

PELBAGAI-LAPISAN MASUKAN-KEHADAPAN

RANGKAIAN NEURAL

Oleh

SALEH ALI ALSHEHRI

Jun 2011

Pegerusi : Profesor Madya Adznan Bin Jantan, PhD

Fakulti : Kejuruteraan

Kanser payudara merupakan salah satu punca utama kematian wanita. Pengesanan

tumor meningkatkan peluang untuk mengatasi penyakit ini. Terdapat beberapa

kaedah diagnostik untuk mengesan tumor, namun begitu masing-masing mempunyai

kekurangannya sendiri. Baru-baru ini, pengimejan Jalur Lebar Ultra (UWB) semakin

diterima berdasarkan beberapa cirinya yang baik, seperti kekhususan dan kekurangan

sinaran pengionannya. Kaedah konfokal merupakan teknik yang paling kerap

digunakan dalam bidang ini, berasaskan tisu payudara homogen dan maklumat

terdahulu mengenai ketelusan tisu. Dengan itu, ia tidak praktikal dan sukar untuk

dilaksanakan secara klinikal.

Tesis ini memberi tumpuan pada perkembangan sistem bukan konfokal lengkap bagi

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pengesanan tumor payudara pada peringkat dengan menggunakan pengimejan Jalur

Lebar Ultra (UWB) berasaskan Rangkaian Neural (NN) yang mengambil kira kedua-

dua tisu homogen dan heterogen. Tugas ini dilakukan dalam dua fasa: i) Simulasi

dan ii) Uji Kaji.

Pada peringkat simulasi, model NN suap depan telah dibangunkan untuk mengenal

pasti kewujudan, saiz dan lokasi tumor di dalam model payudara. Tumor berbentuk

sfera diwujudkan dan diletakkan di lokasi secara rambang di dalam model payudara

hemisfera dengan menggunakan simulator (Computer Simulation Technology CST)

Elektromagnet (EM). Isyarat UWB dipancarkan dan diterima melalui fantom

payudara. Pemancar dan penerima diputarkan 360° untuk mengesan kewujudan, saiz

dan lokasi tumor dalam hirisan payudara dua dimensi dengan menggunakan

peraturan pelengkap terbaik (best-complement rule). Kaedah Analisis Ciri Prinsip

(PFA- Principle Feature Analysis) telah digunakan untuk mengurangkan saiz vektor

khusus dan mengekstrak ciri paling informatik. Kami telah mendapati bahawa nilai

ciri paling informatik berlaku pada isyarat maksimum dan minimum. Ciri yang

diekstrak daripada isyarat UWB dimasukkan ke dalam model NN untuk

membiasakan, mengesahkan dan mengujinya terlebih dahulu dan kemudian untuk

mengesan kewujudan, saiz dan lokasi tumor payudara yang mungkin ada.

Setelah memperoleh keputusan ujian simulasi, satu sistem dibangunkan untuk

mengesan tumor secara eksperimen. Sistem ini merangkumi penghantar-terima

UWB komersil, model NN yang dibangunkan, serta fantom payudara bagi tisu

homogen dan heterogen. Fantom payudara dan tumor dibina menggunakan bahan

dan campuran yang sedia ada yang berkos rendah dengan usaha yang minimum.

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Bahan dan campurannya dipilih menurut nisbah sifat dielektrik tisu payudara.

Pengubah Kosinus Diskret (DCT) isyarat yang diterima telah digunakan untuk

membina vektor khusus untuk membiasakan model NN ini. Akhir sekali, sistem ini

dilatih untuk membezakan antara tumor malignan dan benigna.

Tumor sekecil 0.1 mm dan 0.5 mm (diameter) masing-masing telah berjaya dikesan

melalui simulasi dan perlaksanaan eksperimen. Kadar pengesanan kewujudan, saiz

dan lokasi tumor ialah sekitar (i) 100%, 93%, dan 93.3% dan (ii) 100%, 95.8%, dan

94.3% masing-masing melalui simulasi dan sistem eksperimen. Kemungkinan

pembezaan antara tumor malignan dan benigna juga turut dicapai. Kaedah ini

menggunakan kuasa rangkaian neural dan mempamerkan hala tuju baharu dalam

bidang ini. Ini menjamin pengesanan tumor payudara pada peringkat dan kegunaan

praktikal bagi sistem yang bakal dibangunkan dalam jangka masa terdekat ini.

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ACKNOWLEDGEMENTS

First and most important I thank Allah (SWT) who deserves all the appreciations,

without him nothing would have happened.

I would like to sincerely thank my supervisor Dr. Adznan B. Jantan who gave me

strong support and valuable advice. Also, I would like to thank all of my supervisory

committee members, Dr. Raja Syamsul Azmir Raja Abdullah, Dr. Rozi Mahmood,

and Prof. Zaiki Awang for their continued advice and support. Dr. Raja‘s comments

and supervision provided outstanding guidance for my research work. Special and

strong thanks to Prof. Sabira Khatun for her continued guidance from the first day to

last day in every single part of my research work.

I would like to thank my wife Sara for her patience and support. She may not realize

that taking care of four kids is harder than getting a Ph.D.

I feel that I am lucky to be given the chance to study at UPM, Faculty of

Engineering. The support from the Department of Computer and Communication

System Engineering is much appreciated.

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I certify that a Thesis Examination Committee has met on 15th

June 2011 to conduct

the final examination of Saleh Ali Alshehri on his thesis entitled ―Ultra Wideband

Technique For Breast Cancer Detection Using Multi-Layer Feed-Forward

Neural Networks‖ in accordance with the Universities and University Collages Act

1971 and Constitution of the Universiti Putra Malaysia [P.U.(A) 106] 15 March

1998. The Committee recommends that the student be awarded the degree of Doctor

of Philosophy.

Members of the Examination Committee were as follows:

Abdul Rahman Ramli, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Iqbal Saripan, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Internal Examiner)

Syamsiah Mashohor, PhD

Faculty of Engineering

Universiti Putra Malaysia

(Internal Examiner)

El-Sayed Abdel-Moety Al-Badawy, PhD

Professor

Alexandria Higher Institute of Engineering & Technology (AIET), Egypt

(External Examiner)

BUJANG KIM HUAT, PhD

Professor and Deputy Dean

School of Graduate Studies

Universiti Putra Malaysia

Date :

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfillment of the requirement for the degree of Doctor of Philosophy.

The members of the Supervisory Committee were as follows:

Adznan B. Jantan, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Raja Syamsul Azmir Raja Abdullah, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Member)

Rozi Mahmud, PhD

Associate Professor

Faculty of Medicine and Health Sciences

Universiti Putra Malaysia

(Member)

Sabira Khatun, PhD

Professor

Faculty of Computer Systems and Software Engineering

Universiti Malaysia Pahang

(Member)

Zaiki Awang, PhD

Professor

Microwave center

Universiti Teknologi Mara

(Member)

HASANAH MOHD GHAZALI, PhD

Professor and Dean

School of Graduate Studies

Universiti Putra Malaysia

Date:

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DECLARATION

I declare that the thesis is my original work except for quotations and citations which

have been duly acknowledged. I also declare that it has not been previously, and is

not concurrently, submitted for any other degree at Universiti Putra Malaysia or at

any other institution.

SALEH ALI ALSHEHRI

Date: 15 June 2011

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TABLE OF CONTENTS

Page

DEDICATION ii

ABSTRACT iii

ABSTRAK vi

ACKNOWLEDGMENTS ix

APPROVAL x

DECLARATION xii

LIST OF TABLES xvi

LIST OF FIGURES xviii

LIST OF ABBREVIATIONS xxii

CHAPTER

1 INTRODUCTION 1

1.1 Background 1

1.2 Motivation and Problem Statement 1

1.3 Objectives 7

1.4 Scope of Work 7

1.5 Thesis Organization 11

2 LITERATURE REVIEW 13

2.1 Introduction 13

2.2 Breast Cancer 13

2.3 Ultra Wideband Imaging 15

2.4 Other Techniques 22

2.4.1 Mammography 22

2.4.2 Ultrasound 24

2.4.3 Magnetic Resonance Imaging and Nuclear Imaging 24

2.5 Material Dielectric Properties 25

2.6 Pattern Recognition Techniques 31

2.6.1 Multilinear Regression 36

2.6.2 Decision Tree 38

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2.6.3 Support Vector Machine 38

2.6.4 Neural Networks 39

2.6.5 Feature Reduction 45

2.7 Critical Review of Recent Related Research 47

2.8 Summary 53

3 RESEARCH METHODOLOGY 54

3.1 Introduction 54

3.2 Simulation Process 55

3.2.1 Simulation Software 58

3.2.2 Breast Model and Data Collection 58

3.2.3 Neural Network and Feature Selection 61

3.2.4 Other Pattern Recognition Approaches 85

3.3 Experimental Process 86

3.3.1 The Experimental System Configuration 87

3.3.2 Breast Phantom Construction 89

3.3.3 Neural Network and Feature Extraction 96

3.3.4 Malignant and Benign Tumor Discrimination 101

4 RESULTS AND DISCUSSION 105

4.1 Introduction 105

4.2 Simulation Results 105

4.2.1 Detection Using Signals Associated with Tumor

Location

106

4.2.2 Detection Using Signals Associated with Tumor Size 112

4.2.3 Detection Using Signals Associated with Tumor Size

and Location

116

4.2.4 Feature Reduction Effect on Detection Using Various

Pattern Recognition Approaches

120

4.3 Experimental Results 129

4.3.1 Detection Results Using Homogeneous Breast

Phantoms

129

4.3.2 Detection Results Using Heterogeneous Breast

Phantoms

137

4.3.3 Experimental Malignant and Benign Tissue

Discrimination

140

4.4 Simulation and Experimental Result Comparison 141

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4.5 Comparison with Other Studies 147

5 CONCLUSION AND FUTURE WORKS 151

5.1 Conclusion 151

5.2 Research Contribution 154

5.3 Future Work 154

REFERENCES 156

BIODATA OF STUDENT 168

LIST OF PUBLICATIONS 169