IT 13 052

Examensarbete 30 hpAugusti 2013

Texture Feature Analysis ofBreast Lesions in Automated 3DBreast Ultrasound

Haixia Liu

Institutionen för informationsteknologiDepartment of Information Technology

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Texture Feature Analysis of Breast Lesions inAutomated 3D Breast Ultrasound

Haixia Liu

This thesis investigated a variety of texture features performances on classifying anddetecting breast lesions in automated 3D breast ultrasound (ABUS) images withcomputer-aided diagnosis and detection system. Regions detected by thecomputer-aided detection system could be categorized into benign and malignantclasses, which are supposed to have different texture features.

After normalization and segmentation on the original 3D ultrasound breast imagesautomatically, we implemented four texture feature extraction algorithms on thedetected targets. The proposed four algorithms are based on 3-dimensional gray levelco-occurrence matrix (3-D GLCM), local binary pattern (LBP), Haar-Like and regionalzernike moment (RZM) separately. Three major experiments were carried out on aset of ABUS images. In experiment one, we focused on distinguishing malignantlesions (165 samples) from benign lesions (258 samples). In experiment two, weadded a number of normal cases (150 samples) to the dataset, by grouping them withbenign lesions against malignant lesions and by isolating them from benign andmalignant lesions. In experiment three, we tested texture features ability on reducingfalse positives in the existing computer-aided detection system. In this step, onlynormal cases (5263 samples) and malignant lesions (165 samples) were examined.

To estimate the discrimination power of different texture features, Support VectorMachine (SVM) and AdaBoost classifiers were adopted in corporation withleave-one-patient-out and 10-fold cross validation schemes respectively. The areaunder the receiver operator characteristic (ROC) curve (AUC, also known as Az)values were analyzed corresponding to each texture feature extraction method. TheAz values computed in experiment one are compared as follows: Haar-Like feature'sperformance outweighs others' with the Az value of 0.86, followed by LBP (0.84),RZM(0.81) and 3-D GLCM (0.75). With respect to the results from experiment two,the Az value of grouping normal cases with benign lesions against malignant lesions isbetter than separating them from benign and malignant lesions, in general. Regardingthe outcome from experiment three, the Az value was increased from 0.79 to 0.82after adding LBP and Haralick features to the existing computer-aided detectionsystem.

Based on the overall results, we concluded that texture features are useful onclassifying benign and malignant lesions in ABUS images and they can improve theperformance of the existing computer-aided detection system on detecting breastcancers.

Tryckt av: Reprocentralen ITCIT 13 052Examinator: Ivan ChristoffÄmnesgranskare: Ewert BengtssonHandledare: Tao Tan, Bram Platel and Nico Karssemeijer

Acknowledgements

I would like to thank my thesis reviewer professor Ewert Bengtsson and supervisors Tao,

Bram and Nico for the professional guidance and consecutive support. I would like to

thank Sintorn Ida-Maria and Gustaf Kylberg for introducing RZM algorithm.

Special appreciation to my suppervisors Tao and Bram, who gave me insightful sugges-

tions all through the experiments. Thanks to professor Nico, who gave me the opportu-

nity to work with his group.

Thanks for The Diagnostic Image Analysis Group (DIAG) of the Radiology Department—

Radboud University Nijmegen Medical Centre and Centre for Image Analysis (CBA) of

Uppsala university.

v

Contents

Acknowledgements v

List of Figures x

List of Tables xi

Abbreviations xiv

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Previous work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Overview of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Materials 4

2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 CAD system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Methods 6

3.1 Gray Level Co-occurrence Matrix (GLCM) . . . . . . . . . . . . . . . . . 6

3.1.1 Feature Computation . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.1.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Local Binary Pattern (LBP) . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2.1 Feature Computation . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Haar-Like . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3.1 Feature Computation . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Regional Zernike Moment . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4.1 Feature Computation . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Evaluation and Results 18

4.1 Classifiers and strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2 Results from GLCM features . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.3 Results from LBP features . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

viii

Contents ix

4.4 Results from Haar-Like features . . . . . . . . . . . . . . . . . . . . . . . . 21

4.5 Results from RZM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.6 Results from Combination of GLCM and LBP . . . . . . . . . . . . . . . 24

4.7 Results of false positives reduction . . . . . . . . . . . . . . . . . . . . . . 25

4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5 Conclusion, Discussion and Future Work 28

5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

A Appendix. ROC plots 30

Bibliography 36

List of Figures

2.1 The original 2D transversal slices consists of one cancer centered areshown on the top. The segmented region are shown on the bottom. . . . . 5

3.1 The offset of neighbors 1-8 are: offset1= [1,0], offset2= [1, -1], offset3= [0,-1], offset4= [-1, -1], offset5= [-1,0], offset6= [-1,1], offset7= [0,1], offset8=[1,1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Structure of 3-D GLCM. The examined directions are highlighted by greencircles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.3 Computed area formed by pixels 1-8 and the center pixel Pc. The numberon each cell represents the pixel’s gray value. This is (P=8, R=1) pattern,designated by LBP8,1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.4 The red spot in the center has six neighbors that are represented by greenpoints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.5 In our 3D Breast Ultrasound datasets, generally., a normal case has thesize 271x274x82 (ordered in i, j, k, with computed center point coor-dinates: 107, 51, 41), measured by voxel. The slice in this figure wasobtained under the transversal view lesion-oriented center: j=51. . . . . . 12

3.6 Computing lesion area A. . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.7 2 simple Haar-Like prototypes used in our experiments. . . . . . . . . . . 13

3.8 Demonstration of Haar-Like feature specified by 2 rectangles colored ingreen and red. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.9 (a) demonstrates look-up table elements. (b) shows rules of Calculatingup-left sum areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.10 part of zernike polynomials demonstration. . . . . . . . . . . . . . . . . . 16

4.1 Top 3 Haar-Like features on an image with resizing scheme 2r. . . . . . . 22

4.2 Top 3 Haar-Like features on an image with resizing scheme 3r. . . . . . . 23

4.3 RZM values up to 6 orders regarding normal, benign and malignant cases. 24

4.4 ROC curves of using different type features or different combination offeatures. Compared with ROC curves shown in Appendix, these curveswere post-processed by curve fitting. . . . . . . . . . . . . . . . . . . . . . 26

A.1 ROC curves of GLCM: benign vs malignant . . . . . . . . . . . . . . . . 30

A.2 ROC curves of GLCM: normal vs benign + malignant . . . . . . . . . . . 31

A.3 ROC curves of GLCM: normal+benign vs malignant . . . . . . . . . . . . 32

A.4 ROC curves of LBP: benign vs malignant . . . . . . . . . . . . . . . . . . 33

A.5 ROC curves of LBP: normal vs benign + malignant . . . . . . . . . . . . 34

A.6 ROC curves of LBP: normal + benign vs malignant . . . . . . . . . . . . 35

x

List of Tables

4.1 Performance of 3D-GLCM features(benign vs malignant). . . . . . . . . . 19

4.2 Performance of 3D-GLCM features(normal,benign and malignant). . . . . 20

4.3 Performance of 2D-rotation invariance LBP features(benign vs malignant). 20

4.4 Performance of 2D Rotation Invariance LBP features(normal,benign andmalignant). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.5 Performance of 2D Fuzzy LBP and 3D Fuzzy LBP. . . . . . . . . . . . . . 21

4.6 Performance of Haar-Like features on different views (2r). . . . . . . . . . 22

4.7 Performance of Haar-Like features from different image resizing schemes. . 22

4.8 Performance of RZM features. . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.9 Performance of GLCM,LBP and the combination from GLCM and LBP . 25

4.10 Performance of features. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.11 Performance of different features. . . . . . . . . . . . . . . . . . . . . . . . 27

xii

Abbreviations

CAD Computer Aided Diagnosis

ABUS Automated 3-D Breast Ultrasound

GLCM Gray Level Co-occurrence Matrix

LBP Local Binary Pattern

FLBP Fuzzy Local Binary Pattern

RZM Regional Zernike Moment

Az(AUC) Area Under the Curve

xiv

xv

Chapter 1

Introduction

Second to lung cancer, breast cancer is one of the most common cancers that women suf-

fer from. More than 1.3 million women worldwide are diagnosed with breast cancer each

year [1]. Breast cancer is a progressive disease and screening is very important because

the disease can be cured easier if the tumor can be found early in its course[2] and the

quality of the patient’s life will not be affected due to its non-invasive diagnoses. Mam-

mography has been used as the primary detection modality on breast cancer for years[3].

However, mammography screening is insufficient on detecting noncalcified small cancers

hidden within the dense fibroglandular tissue[4]. Ultrasound assessing, as a complemen-

tary tool to diagnose breast cancer has shown its strength on detecting invasive cancers

in dense breasts[5]. Kolb et al investigated the addition of hand-held ultrasound (HHUS)

device to mammography in examining asymptomatic women with dense breasts[6]. Al-

though hand-held ultrasound device has been used as a well-established diagnostic tool,

it has limitations, such as operator-dependence, which impels radiologists to seek for

more user-friendly and more standardized ultrasound modality. Automated 3D breast

ultrasound (ABUS) is introduced to the capacity of visualizing breasts in 3D. More-

over, it makes inspecting breast on coronal plane possible, which is not available in 2D

ultrasound. Giuliano and Giuliano [7] showed that ABUS in mammographically dense

breasts could improve breast cancer detection in asymptomatic women. In screening,

for each breast, three to five volumetric ABUS views targeting different areas are taken

to cover the whole volume of the breast while usually 2 (MLO and CC) mammographic

views are taken. Therefore ABUS screening reading requires more efforts compared with

mammography screening. To make the screening reading more effective and efficient,

computer analysis is expected to play a role. A computer aided detection (CAD) system

is needed to facilitate the localization of suspicious regions and prevent the oversight

errors by radiologists.

1

Chapter 1. Introduction 2

In a reader study[8], using ABUS led to a significantly higher sensitivity for malignant

lesions than for benign lesions. It is important to develop techniques to help radiologists

on minimizing the number of unnecessary biopsy recalls, by aiding them to distinguish

between benign and malignant lesions using a computer-aided diagnosis system.

1.1 Background

Within breast cancer research institutes world wide, the Diagnostic Image Analysis

Group (DIAG) is a leading research group in computer-aided detection and diagnosis

(CAD), aiming at developing computer algorithms to assist clinicians in the interpreta-

tion of medical images and thereby improve the diagnostic process. This master thesis

project was executed in the DIAG of the Radboud University, in close collaboration with

Fraunhofer MEVIS, which is the largest research and development center for computer

assistance in image-based medicine in Europe. The project also receives supports from

Centre for Image Analysis (CBA), Uppsala University.

1.2 Previous work

Incorporating computer-aided system into clinical workflow is challenging. First, the

computer-aided systems must be automated or require as little intervention from radiol-

ogists as possible. Moreover, due to the time limit on radio-graphical reading, the cancer

detection system should be very sensitive to cancers with limited number of false pos-

itives. In mammography commercial CAD systems currently have about 1 to 3 marks

per case[9, 10]. Moon et al. [11] developed a computer-aided detection system for breast

tumors in ABUS using multi-scale blob detection algorithm. Other achievements (In-

cluding the works for computer-aided detection system and diagnosis system) have been

made in this field ranging from shape analysis[12], speculation investigation (specifically

for classifying benign and malignant lesions in ABUS)[13], border detection, posterior

acoustic behavior observation[14] to texture feature researches[15].

1.3 Motivation

Given that the normalization, segmentation and classification algorithm have been im-

plemented in the DIAG group, this project focused on the utilization of texture features

to extend and improve an existing CAD system for 3D breast ultrasound.

We aimed to achieve the following objectives:

Chapter 1. Introduction 3

• Try different texture features to differentiate benign from malignant lesions and

compare the results to find better features.

• Test the texture features discriminate ability after adding normal cases.

• Combine different texture features to evaluate their performance on discriminating

benign from malignant leions.

• Optimize texture features by tuning parameters.

• Add the optimized texture features to the existing computer-aided detection sys-

tem, to reduce the false positives.

1.4 Overview of the Thesis

The remainder of this report is organized as follows:

• Chapter 2: Introduces the datasets that were used in the experiments.

• Chapter 3: Different texture feature extraction methods are described, including

GLCM, LBP, Haar-Like and RZM.

• Chapter 4: Incorperation of two texture features (GLCM and LBP) to the existing

system is discussed.

• Chapter 5: The results from different algorithms and strategies were presented.

• Chapter 6: Conclusion, discussion and future work.

Chapter 2

Materials

The ABUS images used in this work were obtained from four different institutes, which

are Nijmegen Medical Center (Nijmegen, The Netherlands), the Jeroen Bosch Ziekenhuis

(Den Bosch, The Netherlands), the Falun Central Hospital (Falun,Sweden), and the

Jules Bordet Institute (Brussels, Belgium). There are two types of ABUS systems:

SomoVu automated 3D breast ultrasound system developed by U-systems (Sunnyvale,

CA, USA) and ACUSON S2000 automated breast volume scanning system developed

by Siemens (Mountain View, CA, USA). The image size varies from device to device.

Images acquired by U-systems have a maximum size of 14.6 cm by 16.8 cm on the

coronal plane and a maximum depth of 4.86 cm whereas images acquired by Siemens

have a maximum size of 15.4 cm by 16.8 cm on the coronal plane and a maximum

depth of 6 cm. The fixed frequency of the U-systems transducer is ranging from 8.0

MHz or 10.0 MHz while the frequency of the transducer by Siemens is between 5.0 and

14.0 MHz, and it is adjustable according to the breast size. The 3D volumetric view

obtained by U-systems has a minimal voxel size of 0.29 mm (along the transducer) by

0.13 mm (in depth direction) by 0.6 mm (along the sweeping direction) while images

from Siemens devices have a minimum voxel size of 0.21 mm by 0.07 mm by 0.52 mm.

In preprocessing stage, we resize all images to 0.6 mm cubics, that is to say, 0.6 mm x

0.6 mm x 0.6 mm according to x, y, z coordinate system.

2.1 Datasets

In step one, we focused on distinguishing malignant from benign lesions. There were

258 benign images and 165 malignant images. In step two, we added 225 normal images

to the dataset used in step one. In step three, we only analyzed normal and malignant

cases to decrease the false positive rate. There were 5428 images from 238 patients

4

Chapter 2. Materials 5

including 165 malignant regions and 5263 normal cases detected from a initial stage of

a computer-aided detection system. Malignant lesions were confirmed by biopsies.

2.2 CAD system

In this thesis, we are going to add texture features to the computer-aided detection

(CAD) system. The developed CAD system for detecting breast cancer is based on the

previous work, which utilized variety of features that were studied by Tan et al [13].

In the multi-stage system, segmentations of the breast, the nipple and the chestwall

were performed, providing landmarks for the detection algorithm. Subsequently, voxel

features characterizing coronal spiculation patterns, blobness, contrast, and depth were

extracted. In this thesis, we were doing research on breast lesion texture feature analysis

specifically.

Computation of features requires an accurate delineation of the abnormality, thus, seg-

mentation is an important process in lesion classification. Various segmentation meth-

ods have been proposed to segment lesions in different modalities [16–19]. To obtain a

reliable and computationally efficient region segmentation method we used the a spiral-

scanning based dynamic programming technique, which was originally introduced by

Wang et al.[17] for pulmonary nodule segmentation in CT and later adopted in ABUS

[13] for lesion segmentation. An example is shown in Fig. 2.1.

Figure 2.1: The original 2D transversal slices consists of one cancer centered areshown on the top. The segmented region are shown on the bottom.

Chapter 3

Methods

In this chapter, different methods of texture feature extraction were described. Evalu-

ation and Results from each algorithm were presented. Texture feature algorithms can

be grouped into four categories: structural, statistical, transform and model based. The

represented methods are, mathematical morphology, co-occurrence matrix, Gabor and

fractal and stochastic, correspondingly.

3.1 Gray Level Co-occurrence Matrix (GLCM)

3.1.1 Feature Computation

• Two-Dimension Grey Level Co-occurrence Matrix (2D-GLCM)

The traditional 2D-GLCM defined by Haralick is generated by analyzing the frequencies

of pixels pairs with certain distance and angle. The cell value on the matrix is denoted

by the frequency Pij , which is shown in the following formula:

Pij(d, )= (Pa,Pb)

Where d and represent the distance and angular between pixel pairs Pa, Pb. The gray

values should satisfy I(Pa) = i and I(Pb) = j. Haralick demonstrated the criteria on

selecting pixel pairs in paper [20]. In order to better introduce our 3D-GLCM, we further

state the properties of pixel pairs by labeling every pixel with offset. In the context of

radius=1, which is measured by pixel unit from the center point to its neighbor , for a

centered pixel P0, there are eight neighbors: 1-8. Assume P0 is located on the origin

of coordinates formed by X and Y axis, thus, the eight neighbors surrounding P0 can

be designated as: Offsets = [offsetpn]. Where n is ranging from 1 to 8. offsetpn= [x, y],

6

Chapter 3. Methods 7

where x and y are the horizontal and vertical coordinates of pixel Pn. Note that the

scalar unit is one pixel, instead of the absolute geometry spacing, which is measure in

millimeter from the sonagraph point of view. Fig.3.1. gives detail information of each

neighbor’s offset.

Figure 3.1: The offset of neighbors 1-8 are: offset1= [1,0], offset2= [1, -1], offset3=[0,-1], offset4= [-1, -1], offset5= [-1,0], offset6= [-1,1], offset7= [0,1], offset8= [1,1].

Let xp1, yp1 represent pixel P1s X and Y coordinates; xp2, yp2 represent pixel P2s X and

Y coordinates. In computation, we only consider the pixel pairs that satisfy the following

criteria: (1)xp1=0, yp1=- yp2; (2)xp2=0, yp1=- yp2; (3)yp1=0, xp1=- xp2 (4)yp2=0, xp1=-

xp2 (5)xp10 and xp20 and yp10 and yp20 and xp1 = - xp2 and yp1 = -yp2 Given a distance

between pixel pairs, a rectangular is formed with the fixed distance. When extending

the model into larger space, where neighbors are sitting on the points with x and y

coordinates bigger than one pixel, we exclude all the pixels that are not on the edge of

the rectangular.

• 3D-GLCM structure

Based on the introduction of GLCM in 2D mode and the offsets concepts illustrated

above, the structure of 3D GLCM could be demonstrated as Fig.3.2

Suppose the centered pixel is on the intersection of the X-Y-Z coordinates, thus, it has

26 nearest neighbors in a region of 3x3x3 box, containing 13 pixel pairs. Given a certain

radius r between the center point and its neighbor, a set of Offsets are Formed. Let P1

and P2 be the pixel pairs with each offset: offsetp1[x1, y1, z1] and offsetp2[x2, y2, z2],

which are restricting with the following criteria: For a maximum radius rmax. the co-

ordinates of offsetp1 could be described as: offsetp1−1[-rmax, -rmax, rmax], offsetp1−2[0,

-rmax, rmax], offsetp1−3[rmax, -rmax, rmax], offsetp1−4[-rmax,0 , rmax], offsetp1−5[0,

Chapter 3. Methods 8

Figure 3.2: Structure of 3-D GLCM. The examined directions are highlighted bygreen circles

0, rmax], offsetp1−6[rmax, 0, rmax], offsetp1−7[-rmax, rmax, rmax], offsetp1−8[0, rmax,

rmax], offsetp1−9[rmax, rmax, rmax], offsetp1−10[-rmax, rmax, 0], offsetp1−11[0, rmax, 0],

offsetp1−12[rmax, rmax, 0], offsetp1−13[rmax, 0, 0]. According to algebra theory, every

point listed above has its origin-symmetry pair partner with certain coordinates offsetp2-

*, which could be obtained by changing the sign of the original point’s coordinates. For

example, the partners of points 1, 2, 3 are 3, 2, 1, respectively. Note: in offsetp1-*, *

is ranging from 1-13, which are the pixel points labeled in Fig. 2. In this sense, we

only evaluate 5 pairs (labeled by green circle in Fig.3.2) given a certain radius rmax.

The angle interval is 45 degree. In experiments, radius from 1-6 (measured in pixel) are

investigated. In our datasets, the lesion diameter is ranging from 8 mm to 21.8 mm,

with voxel space 0.6 mm. In order to make use of the dataset extensively, We confine the

radius within 6 pixels. The edge length of the minimum bounding box is calculated with

the formulation: L = Min(Lx, Ly, Lz), Where Lx, Ly and Lz represent the diameter

of a lesion in 3D mode that is segmented with method[13]. Six features are extracted

and analyzed: Energy, Entropy, Inverse Difference Moment, Inertia(contrast), Cluster

Shade, Cluster Prominence.The final features were integrated in such a way that all

features generated from different directions and distances were arranged in a single long

vector, instead of being averaged.

3.1.2 Experiments

Three general experiments were conducted. In experiment one, focusing on benign and

malignant cases; In experiment two, grouping the normal cases with benign and in

experiment three, normal cases were separated from benign and malignant. 6 distances

Chapter 3. Methods 9

(radius ranging from 1 voxel to 6 voxels) and 13 directions were examined during all the

experiments.

3.2 Local Binary Pattern (LBP)

3.2.1 Feature Computation

• 2D LBP

In paper[21], Ojala first introduced Local Binary Patter. Considering a centered pixel

point with 8 neighborhoods computed area, shown in Fig.3.3

Figure 3.3: Computed area formed by pixels 1-8 and the center pixel Pc. The numberon each cell represents the pixel’s gray value. This is (P=8, R=1) pattern, designated

by LBP8,1

The LBP histograms are generated in the following process:

Binary number generating:

Compare each pixel’s gray level intensity value with the value of Pc according to a

certain sequence. If the Pcs value is greater than its neighbor’s, binary value is set to 1,

otherwise, binary value is set to 0. In our experiment, offset was under consideration.

If the gray level difference is bigger than the offset, binary value is set to 1, otherwise,

binary value is assigned with 0. In Fig.3.3, 8-digit binary numbers can be generated in

our method. Start from the Left-Top pixel along a clockwise circle, the 8-digit binary

numbers are:

0 0 1 1 1 0 1 0

Chapter 3. Methods 10

Do the Binary number generating for each pixel on the region of interest (ROI) area on

the image.

Rotation invariance value computing:

To avoid side effects caused by rotation, Ojala gave descriptions on how to generate a

unique descriptor with a minimum output value for a certain pattern. For the detailed

algorithm that is generating unique forms, refer to the paper[22]. For the pattern P8, 1,

36 unique rotation invariance values are:

0,1,3,5,7,9,11,13,15,17,19,21,

23,25,27,29,31,37,39,43,45,47,51,

53,55,59,61,63,85,87,91,95,111,119,127,255

Making Histogram:

By counting the frequencies of each pattern that is represented by a certain unique

rotation invariance value, a histogram over the whole ROI on each image.

Histogram Normalization:

In real cases, for example, the size of the breast cancer lesion varies from one to another.

Normalization is needed to achieve consistency between images that are obtained in

different conditions and between images that are containing different size of targets.

Finally, the feature vectors are formed by the normalized histograms.

• Fuzzy LBP (FLBP)

In order to improve the robustness to noise, new LBP-based algorithms[23] are emerging,

such as median binary patterns (MBP), local ternary patterns (LTP), improved LTP

(ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP)[24] Lakovidis[25]

applied FLBP on Characterizing Ultrasound Texture and obtained promising results.

There are two membership functions that need to be considered: m0(i) and m1(i)[24]

From the membership functions, we can see that the rule to generate binary code differs

between traditional LBP and FLBP. Given a certain neighborhood, each LBP code in

this neighborhood contributes to one or more than one FLBP histogram(s) and the total

contribution of the neighborhood to the FLBP bins is 1.

• 3D LBP

Chapter 3. Methods 11

Figure 3.4: The red spot in the center has six neighbors that are represented by greenpoints.

Define a simple 3D LBP descriptor with one centered point surrounded by 6 neighbor-

hoods. The demonstration of this structure is in Fig.3.4

Similar to the algorithm that generates 2D LBP binary values, the method to get the

6-Digit 3D LBP binary numbers is also by comparing gray values between the centered

point and its neighbors. In J. Fehrs papers[26], possible algorithms based on spherical

harmonics are introduced. He also stated how to select points from a sphere[27].

3.2.2 Experiments

To analyze the performance of LBP, experiments were conducted on 3D breast lesion

ultrasound datasets, which contain 225 normal cases, 258 benign cases and 165 malignant

cases.

Experiment 1.

In this experiment, we focused on benign and malignant lesions separation. 2D LBP with

neighbors 8 and distance 1 was used to generate the 36 rotation invariance histogram

bins. To speed up the computation, we only compute the features within the segmented

area. Moreover, lesion centers in each plane (considering 3 views in a 3D volume data

case) were calculated using gravity-center algorithm. Given the coordinates of the center,

3 lesion-centered planes were obtained from each 3D volume data, achieving relatively

fully use of the data. By concatenating 36 2D-LBP8, 1 texture features from each view,

the total feature vectors are extended to 108 for every single lesion case.

Experiment 2.

Chapter 3. Methods 12

225 normal cases were added to experiment 1. First, we grouped normal cases with

benign cases, to text the features ability of identifying cancer; Further, benign and

malignant cases were merged together against normal cases, to see if texture feature can

discriminate abnormal lesions.

Experiment 3.

Using non-rotation invariance Fuzzy 3D-LBP method, 64 texture feature vectors were

computed according to 6 neighborhood, shown in Fig.3.4. In this experiment, we only

focused on benign and malignant lesions.

3.3 Haar-Like

Possessing certain similarities with Haar-Transform, a digital image features so called

Haar-Like Features have been used for decades in pattern recognition field[28]. Haar-

Like features are well-known in face detection and recognition [29–31]. Here we are

aiming at exploring the capacity of basic Haar-Like features on diagnosing ultrasound

breast lesions, which need to be classified into benign and malignant categories.

3.3.1 Feature Computation

• 2D image obtaining and resizing

After segmentation using algorithm in [13], we obtained lesion targets upon which 3

gravity centers were computed corresponding to coronal view, transversal view, and

sagittal view. Each gravity center defined the position where we cut the 2D slices. An

example of a benign case slice from transversal is presented in Fig.3.5

Figure 3.5: In our 3D Breast Ultrasound datasets, generally., a normal case has thesize 271x274x82 (ordered in i, j, k, with computed center point coordinates: 107, 51,41), measured by voxel. The slice in this figure was obtained under the transversal view

lesion-oriented center: j=51.

Let A represent the lesion area in one 2D slice image and it is approximated by an area

of a circle with diameter r. A was computed simply by counting the number of pixels

that a lesion covers, see Fig.3.6

Chapter 3. Methods 13

Figure 3.6: Computing lesion area A.

The edge length (e) of a square boxing the lesion is flexible, ranging from 1.5*r to 3*r,

in our experiments. Edge length e varies from case to case, depending on the lesion size.

We resize all the 2D slices to 24x24 images. For obtaining 3*r samples,if the edge is

exceeding the one of the four boundaries, we will exclude that sample. For 2*r samples,

we keep all the cases (in order to compare different texture feature’s performance, we

have to do experiments on the same dataset) even if some cases are out of boundaries.

The outer areas were filled with gray value 0. Note that the boundaries are formed

according to the lesion size.

• Image gray level standardize

To avoid side effects caused by different data acquisition when obtaining the ultrasound

volumes, normalizing pixel values is necessary. We performed globle normalization on

the 2D images.

• Haar-Like feature prototypes

In our experiments, we only consider the 2 simple Haar-Like prototypes, which are

demonstrated in Fig.3.7. For more complicated and extended prototypes, refer to[32]

Figure 3.7: 2 simple Haar-Like prototypes used in our experiments.

• Rectangle Parameters

A single Haar-Like pattern is composed of certain rectangles, which are specialized by

the following parameters:width(w) and hight(h); top-left coordinates (x, y); sign: {-1,

1}; An example of Haar-Like prototype shown In Fig.3.8 has 2 rectangles. The green

rectangle has the following parameters: wg=1 and hg=4; (x,y): (0,0); Respectively, the

parameters of the the red rectangle are: wr=1 and wg=4; (x,y): (1,0).

Chapter 3. Methods 14

Figure 3.8: Demonstration of Haar-Like feature specified by 2 rectangles colored ingreen and red.

• Integrated image

To avoid visiting each pixel when extracting Haar-Like features, Viola and Jones took

advantage of building look-up tables that are so-called Integrated Images[33].

Give a certain position on an image, the up-left area with respect to this position en-

compasses all the pixels of the designated integral image. In Fig.3.9(a), take position

I for example, the sum of pixels within rectangle RICAG forms an integral image in

relation to element I, which we name it as IntgI. Similarly, element E in IntgI embraces

the sum of pixels inside rectangle REBAD. Thus, the computation of a rectangular area

is realized by looking up four tables that are specified by four vertexes of the rectangle.

Figure 3.9: (a) demonstrates look-up table elements. (b) shows rules of Calculatingup-left sum areas.

The sum area of rectangle RIFEH in Fig.6.(a) is calculated:

RIFEH=RICAG+REBAD-RFCAD-RHEDG.

Chapter 3. Methods 15

• Algorithm of computing Haar-Like features in a single image given one pattern.

Aglorithm 1: Haar-Like Feature computing

Init: set up parameters of each rectangle in the pattern, save them into a variable Prec

Step.0. construct Integrated Image Imgint

Step.1. compute total number of features

Step.2. compute each feature information according to Prec and make it stored into Pfea

Step.3. compute Haar-Like features based on Prec , Imgint Pfea.

3.3.2 Experiments

Only prototypes shown in Fig.3.7 were adopted in our experiments. For a image with size

24x24, the total number of features is 86400. We only analyzed benign and malignant

cases.

3.4 Regional Zernike Moment

Apart from the well-established texture feature analysis, ranging from Gray Level Co-

occurrence Matrix (GLCM) to Local Binary Patter (LBP), there are innovative ap-

proaches emerging. Regional Zernike Moments (RZM) has been proved to be a compet-

itive texture feature descriptor[34], but there are no records of the RZM application on

identifying ultrasound breast cancer, according to our knowledge. Back to the original

Zernike Moment theory, the magnitudes of the Zernike moments are rotation invariant

and Zernike Polynomials are Orthogonal within unit circle, the merits of which have been

used in object recognition[35, 36] and shape retrieval[37]. Tahmasbi[38] applied Zernike

Moments on mammography images to classify benign and malignant breast mass, re-

sulting Az value up to 0.975. Having noticed the successful cases of applying Zernike

Moments on texture and shape recognition, this chapter will study the Zernike Moments

performance on classifying benign and malignant lesion in 3D breast ultrasound images.

3.4.1 Feature Computation

• Zernike Polynomials

Chapter 3. Methods 16

There are even and odd Zernike polynomials, which are defined as:

Zmn (ρ, ϕ) = Rmn (ρ)cos(mϕ)———even

Zmn (ρ, ϕ) = R−mn (ρ)sin(mϕ)———odd

The radial polynomials Rmn are defined as:

Rmn (ρ) =∑(n−m)/2

k=0(−1)k(n−k)!

k!((n+m)/2−k)!((n−m)/2−k)!ρ(n− 2k)

Several Zernike Polynomials are shown in Fig.3.10, which was created by R. J. Mathar,

at wikipedia 1

Figure 3.10: part of zernike polynomials demonstration.

1http://commons.wikimedia.org/wiki/File:ZernikePolynome4.png

Chapter 3. Methods 17

• Zernike Moments

For the image with NxN size, the discrete form of zernike moments are expressed as the

following:

Zmn = (n+1)λN× (

∑(N−1c=0

∑(N−1r=0 f(c, r)V ∗

n,m(c, r))

• Zernike Moments properties

One of the utilizations of Zernike Moments is the orthogonal property of zernike poly-

nomials. No data redundancy can be attrieved by performing zernike transform on the

image. Another property of zernike polynomials is Symmetries.

• Regional Zernike Moments (RZM)

In paper[34], Kylberg described the algorithm of RZM. By averaging the magnitudes

produced under zernike polynomials, rotation invariant texture features can be obtained.

Note that RZM is calculated within a local patch, instead of the whole image. The size

of the patch can vary from 3x3 to 20x20 pixels when analyzing the texture features from

images with size 200x200. Each RZM feature vector is corresponding to a certain order

and a local interesting area.

3.4.2 Experiments

By dividing the whole image into smaller square windows, where the Zernike Moments

calculation takes place. The averaging results could be the texture feature vectors.

In this experiment, order up to 6 and 32 were tried. Only local patch with size 3x3

were adopted. By concatenating 2D images obtained from 3 orthogonal planes from the

original 3D breast cancer ultrasound images, 45(6 orders) and 144 (32 orders) feature

vectors were generated.

Chapter 4

Evaluation and Results

The configuration of hardware and software used in our experiments are described as

follows: A computer with an Intel(R) Core(TM) i5-3570 CPU 3.40GHz, operating sys-

tem of Ubuntu 12.04, CMake 2.6.4, ITK (InsightToolkit)4.4.0, R Package 2.15.3, Matlab

R2012 were used in our experiments. The average processing time for computing all the

texture features of one case was 11 seconds.

4.1 Classifiers and strategies

To estimate the discriminative ability of texture features on 3D ultrasound breast mass

images, Supporter Vector Machine (SVM) and AdaBoost classifiers were used in experi-

ments. Both SVM and AdaBoost have advantages. For example, SVM can prevent over

fitting when performing certain regularizations, whereas, AdaBoost is a simple and easy

algorithm, considering that there is only one parameter T that needs to be tuned.

In SVM, we adopted radial basis function (RBF) as the kernel. To achieve a rela-

tively unbiased evaluation, leave-one-patient-out cross validation, also called rotation

estimation[42] method was performed on the whole dataset.

In AdaBoost, different iterations were tried. 10 fold cross validation scheme was adopted

in AdaBoost classification process. In optimization procedure, we further divided the

training 9 folds into sub 4 folds, corresponding to 4 different iterations. We used the

same test set to do the evaluation. After optimization, we found out that better results

were obtained when setting the iteration number to 15, compared with 3, 10, 20 and 50.

In the listed results of this chapter, we only show AdaBoost results with iteration 15.

For details, refer to AppendixA.

18

Chapter 4. Evaluation and Results 19

For Haar-Like features, we only used AdaBoost (along with 10 fold cross validation,

because the leave one patient out validation was time-consuming, thus it is not suitable

for Haar-Like features), due to its large amount of feature vectors. The same situation

goes to the experiment ’False Positives Reduction’, which requires thousands of normal

cases. The cases that go to the training set with the same patient name in testing set

were excluded.

For the experiments of analyzing benign and malignant cases using GLCM, LBP and

RZM, the classification results generated from both SVM and AdaBoost were compared.

Receiver operating characteristic (ROC) curve[43] were plotted(we picked the best result

from each texture feature algorithm) and the Area Under the curve (Az) values were

computed.

4.2 Results from GLCM features

The extensive results from 3D-GLCM using SVM were presented, with respect to dif-

ferent distances between two pixel pairs in 3D-GLCM. According to paper[44], angle 90

degree is more discriminative, we also compared the performances of descriptors with

offset[0,0, rmax](this offset denotes direction of 90 degree on x-z plane), rmax is ranging

from 1 to 6. We also compared results from other directions, which are denoted by

Offset( 5,1,3,7,9) and Offset(1-13)1. For the defined 5 directions, refer to Fig.3.2, where

the 5 points are highlighted by green circles. Note that, the 5 points and their partners

are symmetrical over origin. As the results showed that 5 directions performance(Az5)

is better than 1 direction’s(Az1) and 13 directions’(Az13), we further evaluated the 5

directions’ performance using AdaBoost. The results of analyzing benign and malignant

that were yielded under different distances, directions and different classifiers are shown

in Table.4.1.

Table 4.1: Performance of 3D-GLCM features(benign vs malignant).

Radius Az1#SVM Az5#SVM Az13#SVM Az5#AdaBoost

1 0.68 0.73 0.73 0.602 0.67 0.74 0.75 0.673 0.70 0.78 0.76 0.664 0.71 0.75 0.76 0.725 0.70 0.76 0.76 0.596 0.67 0.67 0.75 0.59

The results of adding normal cases using SVM (5 directions in GLCM)are shown in

Table.4.2.1the number represents the voxel shown in Fig.3.2

Chapter 4. Evaluation and Results 20

Table 4.2: Performance of 3D-GLCM features(normal,benign and malignant).

Radius Az5#normal vs benign+malignant Az5#normal+benign vs malignant

1 0.63 0.662 0.67 0.653 0.59 0.744 0.59 0.645 0.58 0.616 0.64 0.63

From the results we can see that radius 3 that is measured in voxel produced better

result. In our case, two voxels with physical distance 3.6 mm can give considerable

texture features. When adding normal cases, the 3D-GLCM features’s discriminate

ability was slightly decreased, but the Az value of 0.74 when grouping normal cases with

benign showed that this feature has potential ability on identifying breast cancers.

Using AdBoost, the Az value (benign vs malignant) of combining of all features from 6

radius with 5 directions is 0.69, not as good as a single feature’s performance generated

on radius 3 (0.78)

4.3 Results from LBP features

Two parameters control the LBP algorithm, which are distance and threshold2. Similar

to GLCM experiments, 6 different distances and 3 different thresholds were evaluated

regarding 2D Rotation Invariance LBP algorithm.

The Az values (benign vs malignant) corresponding to certain distances, thresholds and

classifiers are shown in Table.4.3.

Table 4.3: Performance of 2D-rotation invariance LBP features(benign vs malignant).

Radius Az of threshold 5#SVM Az of threshold 5#AdaBoost

1 0.79 0.722 0.84 0.763 0.81 0.674 0.73 0.655 0.70 0.626 0.67 0.59

Further, two extra Az values were computed with respect to radius 2, threshold 3 and

6, which are 0.77 and 0.80.

2in our experiments, we add an offset when comparing the gray level between the center point andits peripherals.

Chapter 4. Evaluation and Results 21

After adding normal cases, the Az values computed by SVM indirectly (with threshold

5)are shown in Table.4.4.

Table 4.4: Performance of 2D Rotation Invariance LBP features(normal,benign andmalignant).

Radius Az#normal vs benign+malignant Az#normal+benign vs malignant

1 0.68 0.592 0.84 0.723 0.69 0.824 0.70 0.565 0.70 0.706 0.69 0.60

The performances of 2D Fuzzy LBP and 3D Fuzzy LBP with 6-neighborhood were

evaluated. Table.4.5.

Table 4.5: Performance of 2D Fuzzy LBP and 3D Fuzzy LBP.

Radius Az#2D FLBP Az#3D FLBP

1 0.83 0.723 0.83 0.73

There are several conclusions that can be drown from the results. Local patch with

radius 2 is more discriminate. Threshold 5 is better than 3 and 6 when generating the

binary code, meaning, the gray level difference between the center point and its peripher-

als contributes more to a particular pattern if it is considered to be 5. The Az value from

FLBP features is comparable to rotation invariant LBP features. Either because the 3D

breast ultrasound lesions are orientation sensitive or the FLBP algorithm can compen-

sate in some way. 2D FLBP results are Superior to their 3D FLBP counterparts, which

tells that texture features in 3D breast ultrasound images are not as impressive as 2D

images. Az value of 0.73 generated by 3D LBP also showed a considerable discriminate

ability of ABUS image texture features in 3D mode.

4.4 Results from Haar-Like features

With Haar-Like features, we can find discriminate patterns that are specified by certain

coordinates and rectangles in an image.

3 orthogonal planes (transversal view, Sagittal view and Coronal view) were investigated

by Haar-Like features. For each plane, 87400 Haar-Like features were generated and

input into AdaBoost machine to train the classifier. The average of 3 views’ performance

was studied. AdaBoost algorithm can rank weak-learners (features) automatically, which

enabled us to see the best n features in the end.

Chapter 4. Evaluation and Results 22

In this experiment, we also investigated the Haar-Like feature effects caused by different

image resizing strategies (1.5r, 2r and 3r). For the detailed introduction of resizing,

refer to chapter. 3, section 3.3.1. To make them comparable, a portion of images were

selected from the whole dataset. 71 benign, 43 malignant and 16 normal cases were used

in this stage.

Az values obtained from different planes are given in table.4.6.

Table 4.6: Performance of Haar-Like features on different views (2r).

plane Az

Transversal 0.85Sagittal 0.84Coronal 0.82

The Az value of averaging 3 views is 0.86.

Az values calculated from different image resizing strategies are given in table.4.7. Note

that this results were obtained by averaging the performances from 3 views.

Table 4.7: Performance of Haar-Like features from different image resizing schemes.

resize Az

1.5r 0.922r 0.963r 0.96

The top 3 Haar-Like features selected by AdaBoost is shown in Fig.4.1(resizing image

with scheme 2r) and Fig.4.2(3r)

Figure 4.1: Top 3 Haar-Like features on an image with resizing scheme 2r.

Chapter 4. Evaluation and Results 23

Figure 4.2: Top 3 Haar-Like features on an image with resizing scheme 3r.

From the over all results we can see that Haar-Like feature has discriminate power on

classifying benign and malignant breast lesions in 3D ultrasound images.

The patterns generated from transversal view are more discriminating. The average of

3 views performs better than a single view.

From the top 3 Haar-Like features (2r) we can see that one of the best Haar-Like

features covers the whole lesion and the other two reflects lesion boundary effects, which

are different between benign and malignant cases. The best 3 Haar-Like features (3r)

showed the powerful patterns from the lesion surroundings.

4.5 Results from RZM

To verify the discriminate ability of Regional Zernike Moments based features on classi-

fying benign and malignant cases, experiments of extracting RZM features and training

SVM classifier were conducted.

The RZM based texture feature vectors were generated up to 32 orders, from which 6

to 144 feature vectors were recorded. SVM were adopted on the training dataset.

Fig.4.3 shows the RZM values up to 6 orders regarding normal, benign and malignant

cases.

The Az values obtained from different situations are shown on Table.4.8.

Local texture features are characterized by RZM features. With fixed 3x3 windows, ZM

values are averaged all over that image. The Az values(0.72 to 0.81) showed that RZM

Chapter 4. Evaluation and Results 24

Figure 4.3: RZM values up to 6 orders regarding normal, benign and malignant cases.

Table 4.8: Performance of RZM features.

Order Az#transversal view Az#combining 3 Views

6 0.72 0.7312 0.73 0.7532 0.76 0.81

has potential ability to distinguish benign from malignant cases. Higher order can give

better discriminate information than lower order.

4.6 Results from Combination of GLCM and LBP

GLCM features are used widely to analyze texture features statistically and LBP features

are perceived to be a cheaper way to generate features, thus, we further investigated the

performances of combining GLCM and LBP features.

First, by concatenating 30 GLCM features (5 directions, 6 types of features each) from

radius 1 to 6, 180 feature vectors were obtained. Similarly, 648 feature vectors were

generated by 2D rotation invariance LBP algorithm.

Further, all the features from GLCM and LBP were joined together.

AdaBoost classifier was adopted in this experiment. The iteration number was set to

15.

The Az value of combining GLCM is 0.63; combining LBP is 0.69; combining all the

GLCM and LBP features, Az value is 0.74. The combination features’ performance does

not outweigh single features.

Chapter 4. Evaluation and Results 25

Table.4.9.

Table 4.9: Performance of GLCM,LBP and the combination from GLCM and LBP

Radius Az#GLCM Az#LBP

1 0.60 0.722 0.67 0.763 0.66 0.674 0.72 0.655 0.59 0.626 0.59 0.59

combine 0.63 0.69

combine GLCM+LBP 0.71

Note that in this experiment, we only focused on benign and malignant cases.

4.7 Results of false positives reduction

In this experiment, we investigated the utilization of texture features for the classification

between malignant lesions and non-lesion structures which can help reduce false positives

in a computer-aided detection system.

Ultrasound breast lesion or region features are widely investigated such as shape features

[39], spiculation pattern features[13], margin features and posterior acoustic features[14].

Non-texture features incorporated in our CAD systems [40] are extracted from region

shape, coronal spiculation patterns, acoustic behavior, intensities etc. Moreover, contex-

tual features are extracted to suppress false positives surrounding the nipple or behind

the nipple. The distance to the nipple on coronal plane, as a feature was used. Besides,

to reduce false positives beyond the chestwall, a signed distance to the chestwall was

computed. Moreover, the distance to boundary of the foreground mask, the depth of

both the highest and lowest voxel of the region segmentation were also incorporated.

To automatically generate a score that indicates the malignancy of a region, we used

AdaBoost classifier to combine the features since it has its advantage to deal with a

large number of region features and a limited mount of samples (cancers) and it is less

susceptible to the over-fit to the training data. In the training, we used a fixed iteration

number of 503. In order to evaluate the classifier performance and avoid possible bias,

a patient-based 10 fold cross validation scheme was adopted. During the training, we

used all malignant lesions and 500 normal regions randomly selected from the 9 training

folds for training. To investigate the benefits of incorporating texture features, the

3According to literature, 50-iteration can yield better result. But as it showed in our experiments,the better results happend in iteration 15. Tuning this parameter needs more investigation.

Chapter 4. Evaluation and Results 26

experiments were performed with and without using LBP features and GLCM features.

The discriminative performance of the classification was evaluated by computing the

area under Receiver Operating Characteristics (ROC) curve denoted as Az. Statistical

analysis was performed using the fixed-reader with random-cases model. To investigate

the effectiveness of each type of texture features, we computed Az values for LBP and

GLCM separately. We also used Boostrapping[41] to do the statistical analysis and

p-value was plot.

6 most used GLCM features[20] including energy, entropy, inverse difference moment,

inertia, cluster shade and cluster prominence, along with 108 LBP (diameter is 2 pixels,

8-neighborhood) features were added to the existing system.

Table 4.10 shows Az values using different type features on all regions. The Az value of

different feature or combinations varied from 0.58 to 0.82. Using non-texture features

achieved the best result without combinations. With respect to texture features, LBP

features were more discriminative than GLCM features. The ROC curves of the CAD

system before and after adding LBP or GLCM or LBP and GLCM texture features

together are shown in Fig.4.4. The Az value was 0.79 using the existing features, whereas

the Az value 0.82 was generated after adding both texture features.

Table 4.10: Performance of features.

Feature(s) Az(std)

Non-texture features 0.79 (0.02)LBP features 0.58 (0.02)

GLCM features 0.68 (0.02)Non-texture + LBP features 0.69 (0.02)

Non-texture + GLCM features 0.79 (0.02)Non-texture + LBP + GLCM features 0.82 (0.02)

Figure 4.4: ROC curves of using different type features or different combinationof features. Compared with ROC curves shown in Appendix, these curves were post-

processed by curve fitting.

Chapter 4. Evaluation and Results 27

4.8 Summary

GLCM, LBP, Haar-Like and RZM features were extracted and analyzed separately. The

overall results are considerable.

A region classification scheme incorporating local binary patterns (LBP) and gray level

co-occurrence matrix (GLCM) texture features were developed for the classification of

malignant and non-lesion regions in automated 3D breast ultrasound (ABUS). In the

scheme, texture features were added to capture the detail characteristics of cancers.

Using the AdaBoost classifier in combination with 10-fold cross-validation, an Az value

of 0.82 was obtained on a dataset of 165 cancers and 5263 non-lesion regions. It was

found that the performance of classification performance improved when LBP features

and GLCM features were used (p=0.05).

Our results highlight the detection benefits that can be gained by using texture features

such as LBP and GLCM features. Different to previous work using texture features to

classify malignant and benign lesions, we focused on the contribution of texture features

to differentiate cancers from normal regions instead of begin lesions. This classification

is important for a detection system. However, we did not found the benefits of adding

GLCM to non-texture features. The reason might be we did not fully incorporate all

GLCM features and moreover the optimization of offsets which play an important role

in GLCM needed to be further studied. The performance of only using LBP features

is not as good as using non-texture features. However by combining all features, the

performance is significantly improved. In our work, we only extract LBP features inside

the region.

When the best result was selected from each texture feature algorithm, we obtained

table 4.11 shows the comparison.

Table 4.11: Performance of different features.

Feature(classifier) Az

GLCM (SVM) 0.78LBP (SVM) 0.84

Haar-Like (AdaBoost) 0.85RZM (SVM) 0.81

Chapter 5

Conclusion, Discussion and

Future Work

5.1 Conclusion

In this thesis, 4 texture feature algorithms were implemented on 3D breast lesion ul-

trasound images, including Gray Level Co-occurrence Matrix (GLCM), Local Binary

Pattern (LBP), Haar-Like and Regional Zernike Moment (RZM). We mainly focused

on benign and malignant lesions texture feature analysis. Normal cases were added

when using GLCM and LBP features. False positives reduction was investigated after

introducing GLCM and LBP features to the existing system. The discriminate power

of the combination of GLCM and LBP features was estimated. Both Support Vector

Machine (SVM) and AdaBoost classifiers were adopted in the experiments. 10-fold cross

validation and leave-one-patient out schemes were tried. The Az values indicate that

texture features can discriminate benign from malignant lesions and they can improve

the performance of false positive reduction system.

5.2 Discussion

Computer-Aided Detection and Diagnosis systems for detecting and diagnosing breast cancers from 3D ultrasound images require discriminate features. Texture features, especially GLCM and LBP, which give statistical analysis on the images, play an important role on identifying malignant lesions.

Threshold defined by gray level and pixel(voxel) pairs’ distance matter when investigat-

ing texture features.

28

Chapter 6. Conclusion, Discussion and Future Work 29

Benign and malignant lesions have different texture features.

The results were obtained on a very small data set in relation to the number of features.

The performance of the algorithms needs to be verified on a new independent larger

dataset.

5.3 Future work

In the future, we will study the texture features on the lesion boundaries.

GLCM algorithm could be extended to fuzzy mode. 3D LBP rotation invariance can be

attrieved by using spherical harmonics and angular momentum. Haar-Like descriptors

can be established into 3D mode. More options of local window size can be tried in

RZM method.

We will consider to integrate texture features into our existing computer-aided diagnosis

system, to improve the system’s classification performance.

Appendix A

Appendix. ROC plots

Plots of ROC1.

Fig. A.1

Figure A.1: ROC curves of GLCM: benign vs malignant

1generated by SVM.

30

Appendix A. Appendix. ROC plots 31

Fig. A.2

Figure A.2: ROC curves of GLCM: normal vs benign + malignant

Appendix A. Appendix. ROC plots 32

Fig. A.3

Figure A.3: ROC curves of GLCM: normal+benign vs malignant

Appendix A. Appendix. ROC plots 33

Fig. A.4

Figure A.4: ROC curves of LBP: benign vs malignant

Appendix A. Appendix. ROC plots 34

Fig. A.5

Figure A.5: ROC curves of LBP: normal vs benign + malignant

Appendix A. Appendix. ROC plots 35

Fig. A.6

Figure A.6: ROC curves of LBP: normal + benign vs malignant

The Az values shown this group of ROC curves are not exactly the same as what were

described in Chapter 4.2.

2Note that this group of ROC curves were generated on an extension dataset, where there are 190normal cases, 258 benign cases and 171 malignant cases. The results demonstrated in Chapter 4 wereobtained on a relatively limited dataset, where there are 150 normal cases, 258 benign cases and 165malignant cases.

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