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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