Segmentation of Hand Bone for Bone Age Assessment

142 Pages·2013·3.33 MB·English

by  Yan Chai Hum (auth.)

SPRINGER BRIEFS IN
APPLIED SCIENCES AND TECHNOLOGY
Yan Chai Hum
Segmentation
of Hand Bone
for Bone Age
Assessment
SpringerBriefs in Applied Sciences
and Technology
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Yan Chai Hum
Segmentation of Hand Bone
for Bone Age Assessment
1 3
Yan Chai Hum
Universiti Teknologi Malaysia
Skudai-Johor
Malaysia
ISSN 2191-530X ISSN 2191-5318 (electronic)
ISBN 978-981-4451-65-9 ISBN 978-981-4451-66-6 (eBook)
DOI 10.1007/978-981-4451-66-6
Springer Singapore Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013936534
© The Author(s) 2013
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Dedicated to my beloved family and friends
Preface
Bone age assessment is an examination of ossification development with the
purpose of deducing the skeletal age of children to monitor their skeletal devel-
opment and predict their future adult height. Conventionally, it is performed by
comparing left hand radiographs to standard atlas by visual inspection. This pro-
cess is subjective and time consuming. Therefore, automated inspection system,
namely, computer-aided skeletal age scoring system has been established to over-
come the drawbacks. However, the computerized system invariably confronts with
problem in hand bone segmentation, which is the most crucial procedure in the
automated system that determines the success or failure of subsequent processes
of the system. The current manual or semi-automated segmentation frameworks
have impeded the system from becoming truly automated and efficient. The objec-
tive of this book is to provide a solution to large variability in pixel intensity,
uneven illumination, complicated anatomical structure, and artifacts in automated
bone age assessment system. The task is accomplished by first applying the modi-
fied histogram equalized module, then undergoing the proposed automated aniso-
tropic diffusion, followed by a novel fuzzy quadruple division scheme to optimize
the central segmentation algorithm, and finally the process ends with an additional
quality assurance scheme. The designed segmentation framework works without
demanding scarce resources such as training sets and skillful operator. The result
analysis of the resultant images has shown that the designed framework is capable
of separating the soft tissue and background from the hand bone with relatively
high accuracy despite omitting the resources.
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Acknowledgments
Foremost, I am so indebted and would like to thank heartedly my advisor,
Dr. Tan Tian Swee. Under his effective guidance, I have built up my intellectual
foundation on this interdisciplinary field and the proper attitude toward scientific
research. Furthermore, thanks to his consistent motivations, patience, and enthusi-
asm especially in providing me freedom in pursuing my interested research topics.
I am delighted to have such a helpful supervisor that always accessible and willing
to share his knowledge.
Special thanks to Medical Implant Technology Group (MEDITEG), Center
for Biomedical Engineering (CBE), and Universiti Teknologi Malaysia (UTM)
for providing me various resources and stimulating research environment that are
highly conducive to the pursuit of knowledge. Besides, I would like to express
my sincere gratitude to all my research colleagues, friends and all the academics
staffs, as well as the management staffs in my faculty. Their generosity to give val-
uable suggestion and constructive criticism to my works is sincerely appreciated.
Finally, heartfelt gratitude goes to my parents and my family members for all
their supports and endless love in upbringing me. My future pursuit in research
career would not have been possible without their encouragement, blessings, and
inspiration. At this moment of accomplishment, they are those who deserve the
credit. Words fail me to convey my eternal appreciation toward their sacrifices in
various ways throughout my life thus far.
ix
Contents
1 Introduction ................................................ 1
1.1 Introduction ............................................ 1
1.2 Background of the Problem ................................ 3
1.3 Problem Statements ...................................... 3
1.4 Research Objectives ...................................... 4
1.5 Research Scopes ......................................... 5
1.6 Research Contributions ................................... 5
1.7 Book Organization ....................................... 7
References .................................................. 8
2 Literature Reviews .......................................... 11
2.1 Introduction ............................................ 11
2.2 Existing Hand Bone Segmentation ........................... 12
2.3 Thresholding ............................................ 14
2.3.1 Global Thresholding ................................ 14
2.3.2 Local Thresholding ................................. 17
2.3.3 Dynamic Thresholding .............................. 18
2.3.4 Automated Thresholding ............................ 19
2.4 Edge-Based ............................................. 23
2.4.1 Edge Detectors .................................... 24
2.4.2 Edge Linking ..................................... 25
2.5 Region-Based ........................................... 27
2.5.1 Seeded Region Growing ............................. 27
2.5.2 Region Splitting and Merging ........................ 28
2.6 Hybrid-Based ........................................... 29
2.6.1 Watershed Segmentation ............................ 29
2.7 Deformable Model ....................................... 31
2.7.1 Active Contour Model .............................. 32
2.7.2 Active Shape Model (ASM) .......................... 34
2.7.3 Active Appearance Model (AAM) ..................... 38
2.8 Summary .............................................. 40
References .................................................. 40
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xii Contents
3 Design and Implementation ................................... 47
3.1 Introduction ............................................ 47
3.2 The Proposed Segmentation Framework ...................... 48
3.3 Pre-processing .......................................... 49
3.3.1 The Proposed MBOBHE ............................ 50
3.3.2 The Application of Anisotropic Diffusion ............... 61
3.4 The Proposed Adaptive Crossed Reconstruction
(ACR) Algorithm Design .................................. 68
3.4.1 Clustering Algorithm Applied in the Proposed
Segmentation Framework ............................ 69
3.4.2 Automated Block Division Scheme in Adaptive
Segmentation ..................................... 72
3.5 Quality Assurance Process ................................. 87
3.5.1 Gray Level Intensity of Interest
Identification for Elimination ......................... 88
3.5.2 Hand Bone Edge Detection Technique Using Entropy ..... 89
3.5.3 The Area Restoration and Elimination Analysis .......... 92
3.6 Summary .............................................. 96
References .................................................. 98
4 Result and Discussion ........................................ 99
4.1 Introduction ............................................ 99
4.2 Anisotropic Diffusion in the Proposed
Segmentation Framework .................................. 100
4.3 Segmentation Evaluation .................................. 103
4.3.1 User-Specified Parameters ........................... 103
4.3.2 Segmentation Accuracy ............................. 111
4.4 Summary .............................................. 125
References .................................................. 126
5 Conclusion and Future Works ................................. 127
5.1 Conclusion ............................................. 127
5.2 Future Works ........................................... 128
Index ......................................................... 131
Abbreviations
2D Two dimensional
3D Three dimensional
ACR Adaptive crossed reconstruction
AMBE Average mean brightness error
ASD Average structural different
ASM Active Shape Model
BAA Bone age assessment
BARNAE Bounded-area Restoration and Non-bounded Area Elimination
BBHE Bi-histogram equalization
BPS Brightness preservation score
CAD Computer-aided diagnosis
CDF Cumulative density function
CER Clustering efficacy ratio
CHLA Children’s hospital Los Angeles
DPS Detail preservation score
DrDog Derivative difference of Gaussian
DSIHE Dualistic sub-image histogram equalization
FOC Figure of certainty
FOM Figure of merits
FRAG Fragmentation
GHE Global histogram equalization
GLCM Grey Level Co-occurrence Matrix
GP Greulich–Pyle
GVF Gradient vector flow
HDT Homogeneity discrepancy test
HE Histogram equalization
MBOBHE Multipurpose beta optimal bi-histogram equalization
MMBEBHE Minimum mean brightness error bi-histogram equalization
MSE Mean squared error
NBPS Normalized brightness preserving score
NCL Normalized cluster location
NDE Normalized difference expectation
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