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Anthropometric Study of the Femur an Automated Approach

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Anthropometric Study of the Femur an Automated Approach Anthropometric Study of the Femur An Automated Approach Chi Bang Abe LAU July 2009 A Thesis Submitted For The Degree Of Doctor Of Philosophy Surgical & Orthopaedic Research Laboratories Copyright and DAI Statement ’I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.’ Signed: Date: Authenticity Statement ’I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’ Signed: Date: Originality Statement ’I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of mate- rial which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribu- tion made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed: ! " # $%& ' % ! ( ) ) ) " * ( ) ) + , -. -/ 0 -1 0 2/ ! % , 3 4 % % 5 6 7 /.18 + ' !"!# $ Abstract nowledge of anatomy is an elementary step towards the understanding K of the human body. First used by Alphonse Bertillon as an identification system, anthropometry refers to the measurements of human individuals. In orthopaedics, comparative analysis is widely used in the understanding of morphological variance due to races, sex and pathological conditions. The characterization of bone and joint geometry has also been a foundation of modern surgical implant design. Traditional anthropometric studies rely on physical measurements by means of osteometric table. Recent advancements of 3-D imaging modalities and image processing techniques have empowered more fine-grained anthropometric characterization. The inspiration for the study is: • the understanding of anatomy originating from the clinical domain have shown to contribute to undesirable inconsistency in the image processing domain. • the difficulty of existing automated anthropometric methodology in han- dling pathological femur. • the tedious amount of manual and subjective work involved with the increasing amount of high resolution imaging data. The aim of the study is to: • develop a consistent and robust methodology in accurate extraction of anthropometric parameters on the femur. i • increase the level of automation on the process of anthropometric pa- rameter extraction. With the bridging of anthropometry and the image processing disciplines, a robust methodology of anthropometric parameter extraction with high level of automation was developed, implemented and tested. A dataset comprised of femoral CT scans of 19 healthy Australian, 10 healthy Japanese, 15 Japanese diagnosed with primary or secondary hip osteoarthritis and 20 adult sheep was utilized for testing. Intra-class correlation and Cronbach’s α were extensively employed to evaluate the intra-rater, inter- rater and repeated scans consistency of the proposed methodology. High correlation values (mean > 0.95) were noted suggesting a high consistency of the methodology. All healthy and osteoarthritis human datasets were processed successfully. With the structural similarity between the sheep and human femur, the robustness was further demonstrated by accurate processing of the sheep dataset without the need of any modification of the underlying methodology. The methodology proposed is highly automated and requires very few user interactions in the parameter extraction stage. ii Acknowledgments his work would not have been possible without the continue support of T my supervisor, Prof. W.R. Walsh, and all fellow members of the Surgical & Orthopaedic Research Laboratories. I would like to express my sincere gratitude to Dr. Akira Maeyama for his gracious help on acquiring patient data overseas. I cannot say how grateful I am with my parents, for their values, their character and their unfailing support throughout the years. To my lovely sister Angela, for all her encouragements. Last, I wish to thank my dear uncle Simon, aunt Mary, and my cousins Joyce, Patrick and little Winnie for all the care and joy you bring during my stay in Australia. iii iv Contents 1. Introduction 1 2. Anatomy and Bone Histology 5 2.1. Histology of Bone .............................. 5 2.1.1. Types of Cells ............................ 6 2.1.2. Bone Salt .............................. 6 2.1.3. Woven and Lamellar Bone .................... 7 2.1.4. Cortical and Trabecular Bone ................. 8 2.1.5. Modelling and Remodelling ................... 9 2.2. Anatomy of the Human Femur ..................... 10 2.2.1. Hip Bone ............................... 10 2.2.2. Femur ................................ 11 2.2.2.1. Upper End ........................ 11 2.2.2.2. Shaft ............................ 14 2.2.2.3. Lower End ........................ 15 2.2.2.4. Bone Structure ..................... 15 2.2.3. Proximal Tibia ........................... 16 2.2.4. Patella ................................ 16 3. Medical Imaging 19 3.1. Overview ................................... 20 3.2. X-ray Imaging ................................ 20 3.2.1. Principles .............................. 21 3.2.2. Measurement Units ........................ 23 3.2.3. Generation of X-rays ....................... 23 3.2.4. Applications in Radiology .................... 24 v Contents 3.2.5. Biological Hazards ......................... 25 3.2.6. Strengths and Limitations .................... 25 3.3. Computed Tomography .......................... 26 3.3.1. Principle ............................... 26 3.3.2. Hounsfield Unit Scale ....................... 27 3.3.3. Quantitative Computed Tomography ............. 28 3.3.4. Applications in Radiology .................... 28 3.3.5. Biological Hazards ......................... 28 3.3.6. Strengths and Limitations .................... 29 3.3.6.1. Beam Hardening .................... 29 3.3.6.2. Partial Volume Averaging .............. 30 3.3.6.3. Photon Starvation ................... 30 3.3.6.4. Metal Objects ...................... 31 3.3.6.5. Ring Artifacts ...................... 32 3.3.6.6. Helical Artifacts .................... 32 3.4. Dual Energy X-ray Absorptiometry .................. 32 3.4.1. DXA Scores ............................. 34 3.4.2. Biological Hazard ......................... 35 3.4.3. Strengths and Limitations .................... 35 3.5. Magnetic Resonance Imaging ...................... 35 3.5.1. Principles .............................. 36 3.5.2. Biological Hazards ......................... 36 4. Image Analysis 39 4.1. Overview ................................... 40 4.2. Image Acquisition ............................. 40 4.2.1. The DICOM Format ........................ 40 4.3. Image Segmentation ............................ 41 4.3.1. Thresholding ............................ 41 4.3.1.1. Fixed Global Threshold ................ 42 4.3.1.2. Adaptive Threshold .................. 43 4.3.2. Region Growing .......................... 44 4.3.3. Edge Detection ........................... 45 4.3.3.1. Gradient Operators .................. 47 4.3.3.2. Laplacian Operator .................. 51 4.3.3.3. Canny Edge Detector ................. 56 4.3.4. Model Based Techniques ..................... 58 4.4. Image Geometric Transformation .................... 59 vi Contents 4.4.1. Affine Transformation ...................... 59 4.5. Morphological Processing ......................... 62 4.5.1. Preliminaries ............................ 62 4.5.2. Dilation and Erosion ....................... 64 4.5.2.1. Dilation .......................... 64 4.5.2.2. Erosion .......................... 65 4.5.3. Opening and Closing ....................... 66 4.5.4. The Hit-or-miss Operation ...................
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