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A High Performance Computing Approach to Registration in Medical Imaging

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A High Performance Computing Approach to Registration in Medical Imaging A High Performance Computing Approach to Registration in Medical Imaging by Gabriel Mañana Guichón Mentor: Dr. Eduardo Romero Castro A dissertation presented to the National University of Colombia in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Electrical Engineering Bogotá, D.C., Colombia, 2010 AUTHOR’S DECLARATION FOR ELECTRONIC SUBMISSION OF A THESIS I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Gabriel Mañana Guichón 2010 iii Abstract This research has been devoted to the study of the performance related issues associated to automatic registration systems in medical imaging. Registration, in this context, is the determination of a spatial transformation that aligns points in one view of a region of the anatomy, with corresponding points of the same region in another view. One major issue with most image registration techniques is their high computational cost. Because of this, these methods have found limited application to clinical situations where fast execution is required, e.g., intra-operative imaging. High performance can be achieved by reduction in data space, reduction in solution search space and parallel processing. This research has aimed to obtain high performance by taking advantage of grid computing architectures and exploiting inherent parallel techniques such as those found in evolutionary computation. v Acknowledgements I want to especially thank Professor Eduardo Romero, my advisor, for his knowl- edgeable counseling and for always being inspiring. His friendship has been invaluable to me during these years of hard work, as it will be in the years to come. All my love to my partners, Natalia, Juan y Manuel, for their unwaver- ing support and patience. Finally, I also want to thank the National University of Colombia, home, for the scholarship I was awarded and without which the present work could not have been accomplished. vii To my parents, Mabel y Pepe, who passed away recently. Life is a very exceptional situation. – Wim Wenders ix Table of Contents Author’s Declaration iii Abstractv Acknowledgements vii Dedication ix Table of Contents xi List of Tables xv List of Figures xvii 1 Introduction1 1.1 The Image Registration Process......................3 1.2 High Performance Computing.......................6 1.3 Cluster Computing..............................7 1.4 Grid Computing................................8 1.5 Cloud Computing............................... 15 1.6 Problem Definition.............................. 16 1.7 Document Organization........................... 17 2 A Grid Computing Framework for Medical Imaging 19 2.1 Distributed Systems............................. 20 2.2 Space-based Systems............................ 21 2.3 Data Privacy.................................. 27 3 A Distributed Evolutionary Approach Subtraction 29 3.1 Problem Statement.............................. 30 3.2 Parametric Transformations........................ 31 xi 3.3 Similarity Measure.............................. 31 3.4 Optimization Problem............................ 33 3.5 Interpolation Approach........................... 34 3.6 Search Strategy................................ 34 3.7 Algorithm Distribution........................... 36 3.8 Algorithm Validation............................. 37 3.9 The Subtraction Service........................... 41 4 Automatic Registration for Evaluation of PSBM 47 4.1 Introduction.................................. 47 4.2 Image Registration in the Context of PSBM.............. 49 4.2.1 Problem Formulation........................ 51 4.3 Materials and Methods........................... 51 4.3.1 Image acquisition process..................... 52 4.3.2 Preprocessing............................ 52 4.3.3 Geometrical transformations................... 52 4.3.4 Interpolation criterion....................... 53 4.3.5 Similarity measure......................... 53 4.3.6 Optimization method....................... 56 4.3.7 Computational Framework.................... 60 4.4 Results..................................... 64 4.4.1 Validation of the Implemented Algorithms.......... 64 4.5 Discussion................................... 70 5 Curvature-based 3D Non-rigid Image Registration 75 5.1 Introduction.................................. 75 5.2 Problem Statement.............................. 76 5.3 Solution Strategy............................... 76 5.4 Algorithm Distribution........................... 77 5.5 Algorithm Validation............................. 78 5.6 The Registration Service.......................... 79 6 Atlas-based Segmentation Service 81 7 Discussion and Conclusions 85 7.1 Contributions................................. 87 7.2 Discussion................................... 89 7.3 Further Work................................. 89 Appendices 93 xii A Characterization of Tier 3 Sites 95 B National University’s Tier-3 Computing Cluster 97 B.1 Hardware Specification........................... 97 B.2 Software Specification............................ 98 B.2.1 Basic Configuration......................... 98 B.2.2 OSG middleware.......................... 104 B.2.3 gLite middleware.......................... 104 B.3 Cluster Performance............................. 105 C Published Material 107 C.1 Distributed Genetic Algorithm for Subtraction Radiography, GECCO 2006, ISBN: 1-59593-186-4................... 107 C.2 Grid Computing Based Subtraction Radiography, ICIP 2007, ISBN: 978-1-4244-1437-6............................. 107 C.3 Characterization of Tier 3 Sites, CERN 2008, ISBN: 978-92-2083- 321-5...................................... 108 C.4 A Distributed Evolutionary Approach to Subtraction Radiography, Springer-Verlag 2009, ISBN: 978-3-642-10700-9........... 108 C.5 Automatic Registration Method for the Evaluation of Post Surgi- cal Bone Mineralization, submitted to International Journal of Computer Assisted Radiology and Surgery, Springer Verlag 2010. 109 C.6 A Service-Oriented Approach to High-Performace Medical Image Processing, submitted to the International Journal of Medical Informatics, Elsevier 2010......................... 109 C.7 Distributed Curvature-based Non-rigid Image Registration, submit- ted to Transactions on Medical Imaging, IEEE 2010......... 110 Bibliography 111 xiii List of Tables 3.1 Some combinations of rotation, scaling, and translation applied to the set of synthetic images. .............................. 38 3.2 Values found by the Downhill Simplex algorithm. .............. 38 3.3 Values found by the Genetic Algorithm. ................... 39 3.4 Values found by Differential Evolution. .................... 39 3.5 DS-GA-DE performance comparison. .................... 40 4.1 Some combinations of rotation, scaling, and translation applied to the set of synthetic images.......................... 65 4.2 Values found by the Downhill Simplex algorithm........... 66 4.3 Values found by the Genetic Algorithm.................. 66 4.4 Values found by the Differential Evolution algorithm......... 67 4.5 DS-GA-DE performance comparison.................... 68 5.1 Comparison of curvature-based registration algorithms....... 78 xv List of Figures 1.1 PET image slice, CT image slice, PET/CT fusion............1 1.2 Archetypal grid architecture. The easiest way to integrate het- erogeneous computing resources is not to recreate them as ho- mogeneous elements, but to provide a layer that allows them to communicate despite their differences. This software layer is commonly known as middleware..................... 11 1.3 The architecture currently envisaged attempts to bring together the (upward) refinement of data to information and knowledge and the (downward) application of knowledge to information handling and data collection through feedback loop control.... 14 1.4 Cloud Computing taxonomy map..................... 15 2.1 Space-based coordination by means of a minimal programming interface: write, read, take......................... 24 2.2 Space-based computing grid using the replicated-worker design pattern...................................... 25 2.3 Overall architecture of the medical imaging framework, showing the technologies used for communication between neighbouring components: (a) plain Java objects (POJO), (b) HTTP, HTTPS, sockets TCP/UDP, (c) Job Submission Description Language (XML) over sockets TCP, (d) JavaSpaces API, (e) Java Database Connec- tivity (JDBC), (f) sockets TCP, (g) JDBC................. 26 2.4 Data privacy in the Computing Grid layer is attained using the Proxy design pattern............................. 27 3.1 Timing profile for the parallel iterative algorithm showing the percentage time required for each operation.............. 37 3.2 The upper row shows the two images to subtract. Bottom row shows the subtracted images: left without geometrical correction and right after automatic correction................... 44 xvii 3.3 Overall architecture of the subtraction radiography service, show- ing the protocols used for communication between neighbouring components................................... 45 3.4 Graphical user interface for the radiography subtraction service.. 45 4.1 An example of one-point crossover when using binary encoding. Once a crossover point is randomly chosen, the offspring chromo- some will consist of a combination of the two parent substrings. This mechanism can be used to produce one or two offsprings.. 58
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