UNIVERSITI PUTRA MALAYSIA

FARZAN KHATIB

FPSK(p) 2012 21

ENHANCING ULTRASOUND IMAGES FOR BETTER INTERPRETATION OF BREAST CANCER

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ENHANCING ULTRASOUND IMAGES FOR BETTER INTERPRETATION OF BREAST CANCER

By

FARZAN KHATIB

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the

Degree of Doctor of Philosophy

September 2012

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

Doctor of Philosophy

ENHANCING ULTRASOUND IMAGES FOR BETTER INTERPRETATION OF BREAST CANCER

By

FARZAN KHATIB

September 2012

Chair: Prof. Rozi Mahmud, PhD Faculty: Medicine and Health Sciences This work was carried out with the aim to help radiologists in

ultrasonography in diagnosing breast diseases. In particular, the

study was done to improve their efficiency in interpreting results. As

the work concerns with identifying breast disease, the focus is on B-

Mode ultrasound that is suitable for breast. Thus, to improve the

interpretation of results, a multi-purpose multi-tissue breast

phantom is used to capture all the combinations of possible

functions in the B-Mode.

In the first step, ultrasound images were captured using a Quality

multi tissue equivalent Breast Phantom that contains all the needed

specifications. All the possibilities for grey scale images were used on

some ultrasound machines. In order to check the quality of image, a

variety of transducers were used with different frequency responses

and the same frequency setting. This work focused on two lesion

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types, namely, Cyst and Solid, besides other specifications of

Phantom.

A review of previous research on breast disease has shown that most

of them have been done on modalities other than ultrasound,

specifically on mammography and digital mammography. Although

some work has been done on ultrasound, these are rather limited to

very special cases in kidney and abdomen.

In order to enhance ultrasound breast image, image factors such as

Signal to Noise Ratio (SNR) and Receiver Operating Characteristic

(ROC) analysis like sensitivity, specificity, and accuracy were used.

Meanwhile, classification of the masses was done based on the

features that were extracted from two types of images, namely,

phantom images and real human breast images. Then, the proposed

designed was applied to all the images and the output data (benign

or chance of malignancy) were gathered. After classifying the

masses, a set of samples was selected to make the required tests for

the current work, such as the ROC analysis.

As breast disease is one of the deadliest reasons for death among

women in most societies, the aim of the present work was on aiding

the radiologists in checking and detecting some of these

abnormalities. Findings showed an improved sensitivity of 99% and

an enhanced accuracy of 98% for Ultrasound Phantom images.

Evaluation of results for breast Ultrasound images also gave 98.5%

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for sensitivity and 98.2% for accuracy. So a powerful and reliable

Computer Assisted Detection framework was introduced. In addition

to these results, a full machine performance evaluation was done

based on the findings of the proposed routines. Finally, regarding to

this thesis findings, Ultrasound can be a good screening modality as

the first row image modality in breast imaging.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Doktor Falsafah

PENINGKATAN IMEJ ULTRA BUNYI UNTUK MENINGKATKAN KESAN INTERPRETASI KANSER PAYUDARA

Oleh

FARZAN KHATIB

September 2012

Pengerusi : Prof. Rozi Mahmud, PhD Fakulti : Perubatan Dan Sains Kesihatan Kajian ini dijalankan bertujuan untuk membantu ahli radiologi

melaksanakan ujian ultrasonografi dalam mengenal pasti penyakit

payudara. Secara khususnya, kajian ini dijalankan bagi

meningkatkan kecekapan instrumen dalam mentafsirkan keputusan

ujian. Oleh kerana kajian ini berhubung kait dengan kaedah bagi

mengenalpasti penyakit payudara, fokus utama adalah kepada

ultrabunyi Mod B yang sesuai untuk payudara. Justeru, bagi

meningkatkan tafsiran keputusan ujian, tisu pelbagai payudara

phantom pelbagai guna digunakan untuk mencerap semua

kebarangkalian kombinasi fungsi dalam Mod B.

Sebagai langkah permulaan, imej ultrabunyi dirakam menggunakan

tisu pelbagai payudara Phantom berkualiti yang mengandungi semua

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spesifikasi diperlukan. Segala kebarangkalian kepada imej berskala

kelabu digunakan ke atas beberapa mesin ultrabunyi. Bagi

memeriksa kualiti imej, pelbagai transduser digunakan dengan

tindak balas frekuensi yang berbeza dan penetapan frekuensi yang

sama. Kajian ini memberi tumpuan kepada dua jenis lesi iaitu Sista

dan Pepejal di samping spesifikasi Phantom yang lain.

Kajian terhadap penyelidikan mengenai penyakit payudara sebelum

ini menunjukkan bahawa sebahagian besar daripada kajian tersebut

telah dilakukan ke atas kaedah selain daripada ultrabunyi,

khususnya ke atas mamografi dan mamografi digital.

Walaupun beberapa kajian telah dilakukan ke atas ultrabunyi, ia

agak terhad kepada kes-kes terpilih di dalam buah pinggang, perut

dan payudara.

Dalam usaha untuk meningkatkan imej ultrabunyi payudara, faktor

imej analisis Isyarat kepada Nisbah Bunyi (SNR) dan Penerima

Operasi Ciri-ciri (ROC) seperti sensitiviti, pengkhususan dan

ketepatan digunakan. Sementara itu, klasifikasi jisim dibuat

berdasarkan ciri-ciri yang telah diekstrak daripada dua jenis imej

iaitu imej Phantom dan imej payudara. Kemudiannya, satu reka

bentuk cadangan digunakan kepada semua imej dan data output

(benigna atau kehadiran malignansi) yang dikumpulkan. Setelah

klasifikasi jisim dibuat, satu set sampel dipilih untuk dibuat ujian

yang diperlukan dalam kajian semasa seperti analisis ROC.

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Oleh kerana penyakit payudara adalah salah satu penyebab utama

kematian di kalangan wanita dalam kebanyakan masyarakat, kajian

ini dijalankan dengan tujuan utama untuk membantu pakar

radiologi dalam memeriksa dan mengesan sebarang keadaan

abnormal yang wujud. Dapatan kajian menunjukan peningkatan

sensitiviti sebanyak 99% dan juga peningkatan ketepatan sebanyak

98% bagi imej-imej ultrasound phantom. Penilaian terhadap

keputusan imej-imej ultrasound juga memberi peningkatan

sebanyak 98% bagi sensitiviti dan 98.2% bagi ketepatan. Oleh itu,

satu rangka pengesanan menggunakan komputer telah

diperkenalkan. Selain daripada dapatan kajian tersebut, satu

penilaian mesin sepenuhnya juga telah dilakukan berdasarkan hasil

kajian dari rutin yang dicadangkan. Berdasarkan dapatan kajian

ini, ultrasound boleh menjadi satu pengimejan modaliti dan juga

sebagai pengimejan modaliti utama di dalam pengimejan payudara.

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ACKNOWLEDGEMENTS First of all, I would like to express my greatest gratitude to ALLAH

almighty, for his help and support during the course of life and the

moment of truth.

I want to thank my supervisor Prof. Dr. Rozi Mahmud. It has been an

honor to be her Ph.D. student. I appreciate all her contributions of

time and ideas to make my Ph.D. experience productive and

stimulating. The joy and enthusiasm she has for her research was

contagious and motivational for me, even during tough times in the

Ph.D. pursuit.

I would like to thank my supervisory committee members: Assoc.

Prof. Dr. M.Iqbal Saripan, Assoc. Prof. Dr. Raja Syamsul Azmir Raja

Abdullah and Dr. Syamsiah Mashohor for their time, helpful

comments and invaluable guidance and constructive criticisms

throughout the success of this project.

Lastly, I would like to thank my family for all their love and

encouragement; for my parents who raised me and supported me in

all my pursuits. And most of all for my loving, supportive,

encouraging, and patient wife and daughter, Firouzeh and Negin,

whose faithful support during the final stages of this Ph.D., is so

appreciated. Thank you.

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I certify that a Thesis Examination Committee has met on 11 September 2012 to conduct the final examination of Farzan Khatib on his thesis entitled " ENHANCING ULTRASOUND IMAGES FOR BETTER INTERPRETATION OF BREAST CANCER " in accordance with the Universities and University Colleges Act 1971 and the Constitution of the Universit Putra Malaysia [P.U.(A) 106] 15 March 1998. The Committee recommends that the student be awarded the Doctor of Philosophy. Members of the Thesis Examination Committee were as follows: Johnson Stanslas Associate Professor Faculty of Medicine and Health Science Universiti Putra Malaysia Abd Rahman bin Ramli Associate Professor Faculty of Engineering Universiti Putra Malaysia Arsmah Ibrahim Professor Faculty of Mathematics and Computer Science Universiti Technologi Mara SEOW HENG FONG,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 degree of Doctor of Philosophy. The members of the Supervisory Committee were as follows: Rozi Mahmud, PhD Professor Faculty of Medicine and Health Science Universiti Putra Malaysia (Chairman) M.Iqbal Saripan, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Member) Raja Syamsul Azmir b. Raja Abdullah, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Member) Syamsiah bt. Mashohor, PhD Senior Lecturer Faculty of Engineering Universiti Putra Malaysia (Member)

__________________________

BUJANG BIN KIM HUAT, 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. FARZAN KHATIB Date:

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LIST OF TABLES

Table Page 3.1:Selected frequencies on each transducer 81

3.2:Contrast parameter and its examined range 83

3.3:Machines coding 101

3.4:Modes of Operations 101

3.5:Probes (Transducers) 102

3.6:Applied frequencies 102

3.7:Impulse responses of the ideal frequency selective linear phase 120

3.8:Windows of order m 121

3.9:Data for the posterior echo 159

3.10:Mass Detection 164

4.1 (a) First and (b) second radiologist 170

4.2 Actual Image Depth 173

4.3 Posterior result 178

4.4 Edge artifact result 179

4.5 Comparison of Final result 179

4.6 Posterior result 186

4.7 Edge artifact result 187

4.8 Final result 188

4.9 (A) ROC data 191

4.10 (A) ROC data 191

4.11 (B) ROC data 193

4.12 (B) ROC data 193

4.13 (C) ROC data 195

4.14 (C) ROC data 195

4.15 (D) ROC data 196

4.16 (D) ROC data 196

4.17 (E) ROC data 198

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Table Page

4.18 (E) ROC data 198

4.19 (F) ROC data 199

4.20 (F) ROC data 200

4.21 (BASIC) ROC data 201

4.22 (BASIC) ROC data 202

4.23 (TISSUE HARMONY) ROC data 203

4.24 (TISSUE HARMONY) ROC data 203

4.25 (SPATIAL) ROC data 204

4.26 (SPATIAL) ROC data 204

4.27 (COMPOUND) ROC data 205

4.28 (COMPOUND) ROC data 206

4.29 (LFP) ROC data 207

4.30 (LFP) ROC data 208

4.31 (HFP) ROC data 208

4.32 (HFP) ROC data 209

4.33 (OVERALL) ROC data 211

4.34(OVERALL) ROC data 211

4.35 (BREAST) ROC data 213

4.36(BREAST) ROC data 213

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LIST OF FIGURES Figure Page

1.1: The anatomy of breast 2

1.1: The basic anatomy of breast 3

1.3: Normal skin 4

1.4: Calcification in benign epithelium 11

1.5: Stromal calcifications 12

1.6: Calcifications in fibrocystic change 12

1.7: Branching calcifications in ductal carcinoma in situ 12

1.8: Breast tumour 13

1.9: A typical progression of breast tissues 14

1.10: A simple cyst 16

1.11: Invasive Ductal Carcinoma 18

1.12: Stromal fibrosis 19

1.13: Fibroadenoma 20

1.14: Invasive Ductal Carcinoma 21

1.15: Ductal Carcinoma in situ 21

1.16: The mammography imaging chain 27

1.17: Thermal balance 24

1.18: A sample of the ultrasound machine 29

1.19: A linear transducer 34

1.20: The spatial resolution 35

2.1: Original image 54

2.2: Enhanced image 54

2.3: Original image 57

2.4: Enhanced image 57

2.5: A comparison of the 4 imaging techniques 58

2.6: Breast Lesion 68

3.1: General Purpose Multi Tissue Ultrasound Phantom 79

3.2: Machine A with a full screen 84

3.3: Machine B with a full screen 85

3.4: Machine C with a full screen 85

3.5: Machine D with a full screen 86

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Figure Page

3.6: Machine E with a full screen 87

3.7: Machine F with a full screen 87

3.8: The DTHI concept 89

3.9: Ultrasound 89

3.10: Low frequency probe at 6MHz 92

3.11: Low frequency probe at 8MHz 92

3.12: High frequency probe at 9MHz 93

3.13: High frequency probe at 10MHz 93

3.14: High frequency probe at 14MHz 93

3.15: Low frequency probe at 8MHz and contrast 42 94

3.16: Low frequency probe at 8MHz and contrast 60 94

3.17: Low frequency probe at 8MHz and contrast 78 95

3.18: Low frequency probe at 8MHz and contrast 102 95

3.19: The main chart of CAD 98

3.20: Phantom image analyses 100

3.21: Phantom image analyses (cont…) 103

3.22: Contrast Table 105

3.23: A comparison between DICOM and JPG images 107

3.24: Pre-processing and filtering 111

3.25: Contrast stretching 114

3.26: Window filtering 122

3.27: Rectangular window 123

3.28: Hanning window 124

3.29: Hamming window 124

3.30: Blackman window 125

3.31: Segmentation 129

3.32: Segmentation and edge detection 130

3.33: Surface Area 135

3.34: Reduction in the surface area versus radius 135

3.35: Classification 141

3.36: Signature of a rectangle 143

3.37: Signature of an ellipse 143

3.38: Irregular shape 144

3.39: The estimate shape 145

3.40: The horizontal lines 146

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Figure Page

3.41: The estimate shape (cont…) 147

3.42: The vertical lines 148

3.43: The estimate shape (cont…) 150

3.44: Posterior acoustic enhancement behind a benign 152

3.45: Posterior acoustic shadowing behind a solid 152

3.46: A simple Cyst 153

3.47: A simple cyst with a very round shape 153

3.48: Solid Lesions – Benign 154

3.49: Solid Mass – Malignant 155

3.50: Solid benign and its appearance 156

3.51: Solid malignant and its appearance 156

3.52: Posterior shadowing – malignant 157

3.53: A cystic benign with a posterior enhancement 158

3.59: Mass Detection 162

4.1 Comparison of machines performance over LFP 169

4.2 Comparison of machines performance over HFP 169

4.3 Interpolation in Low frequency 172

4.4 Interpolation in High frequency 172

4.5 Split image 174

4.6 Image histogram 174

4.7 Equalized image 175

4.8 2D Median 175

4.9 Contrast stretching 175

4.10 Thresholded image 176

4.11 Filtered image 176

4.12 Filter magnitude response 176

4.13 Center of mass 176

4.14 Detected mass surface 177

4.15 Detected mass 177

4.16 Outline of mass 177

4.17 Mass Signature 178

4.18 Outlined mass 178

4.19 Cystic sample 180

4.20 Split image 181

4.21 Improved contrasts 181

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Figure Page

4.22 Filtered output 183

4.23 Threshold 182

4.24 Filling holes 183

4.25 Estimating mass 184

4.26 Morphological operation 184

4.27 Mass outline 185

4.28 Outlined Mass 185

4.29 Estimating shape 185

4.30 ROC for ROI machine A 191

4.31 ROC for Auto machine A 191

4.32 ROC for ROI machine B 193

4.33 ROC for Auto machine B 193

4.34 ROC for ROI machine C 194

4.35 ROC for Auto machine C 195

4.36 ROC for ROI machine D 196

4.37 ROC for Auto machine D 196

4.38 ROC for ROI machine E 197

4.39 ROC for Auto machine E 198

4.40 ROC for ROI machine F 199

4.41 ROC for Auto machine F 200

4.42 ROC for ROI Basic Mode 201

4.43 ROC for Auto Basic Mode 202

4.44 ROC for ROI Tissue harmony 202

4.45 ROC for Auto Tissue harmony 203

4.46 ROC for ROI Spatial 204

4.47 ROC for Auto Spatial 204

4.48 ROC for ROI Compound 205

4.49 ROC for Auto Compound 205

4.50 ROC for ROI LF probe 207

4.51 ROC for Auto LF probe 208

4.52 ROC for ROI HF probe 208

4.53 ROC for Auto HF probe 209

4.54 ROC of ROI Overall performance 211

4.55 ROC of Auto Overall performance 211

4.56 ROC of ROI Breast image 212

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LIST OF ABBREVIATIONS 3D Three Dimensional

AED Automatic Exposure Control

AUC Area Under Curve

BCDDP Breast Cancer Detection and Demonstration

BMP Bit Map

BP Band Pass

BSE Breast Self Examination

BSGI Breast Specific Gamma Imaging

BUS Breast Ultrasound

CAD Computer Aided Detection

CADG Computer Aided Diagnosis

CE Computer Enhancement

CT Computed Tomography

DCIS Ductal Carcinoma In Situ

DICOM

Digital Imaging and Communications in

Medicine

DPI Dot Per Inch

DTHI Differential Tissue Harmony

EFV Extended Field of View

FCD Fibrocystic Disease

FIR Finite duration Impulse Response

FP False Positive

FTI Fatty Tissue Imaging

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GEN General

GIF Graphic Interchange Format

HC High Contrast

HF High Frequency

HFP High Frequency Probe

HP High Pass

HRT Hormone Replacement Therapy

HVC Human Vision Characteristic

IDC Infiltrating Ductal Carcinoma

IIR Infinite duration Impulse Response

ILC Infiltrating Lobular Carcinoma

IR Infra Red

JPEG Joint Photographic Experts Group

LC Low Contrast

LCIS Lobular Carcinoma In Situ

LF Low Frequency

LFP Low Frequency Probe

LP Low Pass

LUT Look Up Table

MATLAB Matrix Laboratory

MC Medium Contrast

MD Medical Doctor

MRI Magnetic Resonance Imaging

MSE Mean Square Error

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NEMA National Electrical Manufacturers Association

PEN Penetration

PNG Portable Network Graphics

PSNR Peak Signal to Noise Ratio

PSTHI Pulse Subtraction Tissue Harmony Imaging

PWD Pulse Wave Doppler

RAW Raw Image Format

RES Resolution

ROC Receiver Operating Characteristic

ROI Region Of Interest

SF Subcutaneous Fat

SID Source to Image Distance

SNR Signal to Noise Ratio

SRI Speckle Reduction Imaging

TIF Tagged Image File Format

US Ultrasound

VE Virtual Environment

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TABLE OF CONTENTS Page ABSTRACT ii ABSTRAK v ACKNOWLEDGEMENTS viii APPROVAL ix DECLARATION xi LIST OF TABLES xii LIST OF FIGURES xiv LIST OF ABBREVIATIONS xviii CHAPTER 1 INTRODUCTION

Breast Anatomy 1 Breast Cancer 5 Imaging Modality 8 Pathology 10 Breast Masses 13 Cystic Mass 14 Solid Mass 18 Breast Imaging 21 Mammography/Digital Mammography 22 Magnetic Resonant Imaging 23 Infra Red Imaging 24 3D Microwave Imaging 25 Ultrasound 25 Tomosynthesis 27 Nuclear Imaging 28 Ultrasound Machines 29 Modes of Operations 29 B-Mode 31 Transducers 32 Frequency Range and Resolution 35 Problem Statement 37 Aim and Objective 39 Outline of thesis 41

2 MEDICAL IMAGE PROCESSING 43 Introduction 43 Pre-processing 47 Why ultrasound 58

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Page Mass classification and Computer Assisted detection 65 Summary 75

3 MATERIALS AND METHODS 77 Finding a suitable phantom 78 Ultrasound Machines 79 Frequency 80 Contrast 82 Modes of operation 83 Capturing Ultrasound Images 90 System Flowchart 97 Preparing Ultrasound Images 98 Contrast Look-Up Table 103 Input Image File Format 106 Pre-processing and Filtering 109 Histogram Equalization 110 Median Filtering 112 Contrast Stretching 113 Band Pass Filtering 115 FIR Band Pass Filter 116 Windowing Method 119 Segmentation 125 Thresholding 127 Morphological Image Processing 131 Edge Detection 136 Classification 139 Estimating Shape 142 Shadowing and Posterior Echo 151

4 RESULTS AND DISCUSSION 165 Machine Performance 165

Machine A 166 Machine B 166 Machine C 166 Machine D 167 Machine E 167

Machine F 168 Comparison 168 Interpolation 171 Image depth 173 Phantom image output 174 Real Ultrasound Breast image 180

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Page ROC analysis 190

Machine A 191 Machine B 193 Machine C 194 Machine D 196 Machine E 197

Machine F 199 Performance in different modes 200 Basic (Mode B) 201 Tissue Harmony (Mode T) 202 Spatial Compound (Mode S) 203 Compound (Mode C) 205 Probe performance 207 LF Probe 207 HF Probe 209 Over all performance 210 Ultrasound Breast images 213 5 SUMMARY, CONCLUSION AND FUTURE STUDY 215 Summary 215 Conclusion 221 Further studies 223 REFERENCES / BIBLIOGRAPHY 224 APPENDIX A 231 APPENDIX B 242 BIODATA OF STUDENT 243 LIST OF PUBLICATIONS 245