Segmentation and Lesion Detection in Dermoscopic Images Khalid Ahmad A Eltayef

170 Pages·2017·29.02 MB·English

Segmentation and Lesion Detection in Dermoscopic
Images
A thesis submitted as partial fulfillment of the requirement of
Doctor of Philosophy (Ph.D.)
by
Khalid Ahmad A Eltayef
Computer Science Department
Brunel University London
November 2017
Declaration
I, Khalid Eltayef, hereby declare that the work presented in this thesis is completely my
ownandhasnotbeensubmittedforanyotherdegreequalification. Partsoftheworkhasbeen
previously published in conference or journal papers, this has been mentioned in the thesis.
Where I have consulted the work of others, this is always clearly stated.
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List of Publications
The following conferences publications have been produced as a direct or indirect result of the
research that is discussed in this thesis:
1. Eltayef, Khalid, Yongmin Li, and Xiaohui Liu. "Detection of pigment networks in der-
moscopy images." Journal of Physics: Conference Series. Vol. 787. No. 1. IOP Publish-
ing, 2017.
2. Eltayef, Khalid, Yongmin Li, and Xiaohui Liu. "Detection of melanoma skin cancer in
dermoscopy images." Journal of Physics: Conference Series. Vol. 787. No. 1. IOP
Publishing, 2017.
3. Eltayef, Khalid, Yongmin Li, and Xiaohui Liu. "Lesion segmentation in dermoscopy
images using particle swarm optimization and markov random field." International Sym-
posium on Computer-Based Medical Systems - (IEEE CBMS 2017).
4. Eltayef,Khalid,YongminLi,BashirI.Dodo,andXiaohuiLiu. "SkinCancerDetectionin
Dermoscopy Images Using Sub-Region Features." Intelligent Data Analysis (IDA 2017).
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This thesis is dedicated with endless love and gratitude to: my wife Sara who has been
always supportive, encouraging despite worked hard to see that I succeed in life, and my
beautiful daughters Nadeen and Maryia who with their love and jokes brought happiness and
cheerfulness to my life.
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Abstract
Malignant melanoma is one of the most fatal forms of skin cancer. It has also become
increasingly common, especially among white-skinned people exposed to the sun. Early de-
tection of melanoma is essential to raise survival rates, since its detection at an early stage
can be helpful and curable. Working out the dermoscopic clinical features (pigment network
and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate
methodofreachingthecorrectclinicaldiagnosis, andensuretherightareareceivesthecorrect
treatment. These structures are considered one of the main keys that refer to melanoma or
non-melanoma disease. However, determining these clinical features can be a time-consuming,
subjective (even for trained clinicians) and challenging task for several reasons: lesions vary
considerably in size and colour, low contrast between an affected area and the surrounding
healthy skin, especially in early stages, and the presence of several elements such as hair,
reflections, oils and air bubbles on almost all images.
This thesis aims to provide an accurate, robust and reliable automated dermoscopy image
analysistechnique, tofacilitatetheearlydetectionofmalignantmelanomadisease. Inparticu-
lar, four innovative methods are proposed for region segmentation and classification, including
two for pigmented region segmentation, one for pigment network detection, and one for lesion
classification. In terms of boundary delineation, four pre-processing operations, including Ga-
bor filter, image sharpening, Sobel filter and image inpainting methods are integrated in the
segmentation approach to delete unwanted objects (noise), and enhance the appearance of the
lesion boundaries in the image. The lesion border segmentation is performed using two alter-
native approaches. The Fuzzy C-means and the Markov Random Field approaches detect the
lesion boundary by repeating the labeling of pixels in all clusters, as a first method. Whereas,
the Particle Swarm Optimization with the Markov Random Field method achieves greater
accuracy for the same aim by combining them in the second method to perform a local search
and reassign all image pixels to its cluster properly. With respect to the pigment network
detection, the aforementioned pre-processing method is applied, in order to remove most of
thehairwhilekeepingtheimageinformationandincreasethevisibilityofthepigmentnetwork
structures. Therefore, aGaborfilterwithconnectedcomponentanalysisareusedtodetectthe
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pigment network lines, before several features are extracted and fed to the Artificial Neural
Network as a classifier algorithm. In the lesion classification approach, the K-means is applied
to the segmented lesion to separate it into homogeneous clusters, where important features
are extracted; then, an Artificial Neural Network with Radial Basis Functions is trained by
representative features to classify the given lesion as melanoma or not. The strong experimen-
tal results of the lesion border segmentation methods including Fuzzy C-means with Markov
Random Field and the combination between the Particle Swarm Optimization and Markov
Random Field, achieved an average accuracy of 94.00% , 94.74% respectively. Whereas, the
lesion classification stage by using extracted features form pigment network structures and
segmented lesions achieved an average accuracy of 90.1% , 95.97% respectively. The results
for the entire experiment were obtained using a public database PH2 comprising 200 images.
The results were then compared with existing methods in the literature, which have demon-
strated that our proposed approach is accurate, robust, and efficient in the segmentation of
the lesion boundary, in addition to its classification.
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Acknowledgements
First of all I would like to express my sincere gratitude to my supervisor, Dr. Yongmin
Li (Brunel University) for his significant advices, mentor-ship, unconditional support and
encouragementthroughoutmyPhDresearch. Ourdiscussionsalwaysgavemetheconfidenceof
beingontherightpath. IalsoowespecialthankstoProfessorXiaohuiLiu(BrunelUniversity),
mysecondsupervisor, forhissuggestionsandencouragingcommentsthroughoutthisresearch.
Special thanks to my wife Sara and my beautiful princesses Nadeen and Mariya who have
been hugely supportive throughout the four years it has taken to achieve this work. Also I
thank all my family and all my relatives in Libya for their continued support.
Finally, I cannot forget to thank all my colleagues for their support including Faris Alwz-
inani, Mahir Arzoky, Bashir Dodo, Mashael Al-luhaybi, Liela Yousefi, Nadia Hussain, Weibo
Liu, Najeeb Gambo, Khalipha Abubaker, and all PhD student. As well as thank all the lec-
tures and stuff in Computer Science Department. Also I would like to thank Aljabal Algharby
University for giving me the opportunity to study in the UK.
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Contents
1 Introduction 1
1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Dermoscopy Image Analysis and Early Detection of Melanoma Disease . . . . . 3
1.3 Dermoscopic Image Structures Extraction . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Challenges in Dermoscopic Image Structures Detection and Skin Lesion
Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Thesis Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 Thesis Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.7 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.8 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Background of Dermoscopy Images and Literature Review 14
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Dermoscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Dataset Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 An Overview of an Image Segmentation and Previous Works . . . . . . . . . . 19
2.5 Previous Works Related to Lesion Segmentation . . . . . . . . . . . . . . . . . 20
2.6 Previous Methods Related to Skin Lesion Classification . . . . . . . . . . . . . 26
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3 Segmentation of Pigmented Regions in Dermoscopy Images 32
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Overview of the FCM and MRF Methods . . . . . . . . . . . . . . . . . . . . . 34
3.2.1 The FCM Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.2 The MRF Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3.1 Dermoscopic Image Pre-processing . . . . . . . . . . . . . . . . . . . . . 37
3.3.1.1 Reflection Detection . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.1.2 Hair Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.1.3 Image Inpainting . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.2 Skin Lesion Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Lesion Segmentation in Dermoscopy Images Using Particle Swarm Opti-
mization and Markov Random Field 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Overview of the PSO Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3.1 Dermoscopic Image Pre-processing . . . . . . . . . . . . . . . . . . . . . 58
4.3.2 Skin Lesion Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5 Detection of Pigment Network Structures in Dermoscopy Images 73
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2.1 Image Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2.2 Pigment Network Detection . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2.2.1 Network Enhancement . . . . . . . . . . . . . . . . . . . . . . 77
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5.2.2.2 Network Detection . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.3 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2.4 Lesion Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3 Experimental Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6 Skin Cancer Detection in Dermoscopy Images Using Sub-Region Features 93
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2.1 Pre-Processing of Dermoscopic Images . . . . . . . . . . . . . . . . . . . 95
6.2.2 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.2.2.1 Skin Lesion Segmentation . . . . . . . . . . . . . . . . . . . . . 96
6.2.2.2 Sub-Region Clustering . . . . . . . . . . . . . . . . . . . . . . . 96
6.2.3 Feature Extractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.2.3.1 Colour Moments . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.2.3.2 Colour Histogram . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.2.4 Skin Lesion Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.3 Experimental Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7 Conclusion and Future Work 108
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.2 Research Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.2.1 Region Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.2.2 Pigment Network Detection . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2.3 Sub-Region Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 113
7.3 Research Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
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