published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Mathematics / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

by Ho, Chi-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 62-64). / Chapter 1 --- Introduction / Chapter 2 --- Previous Work --- p.2-1 / Chapter 2.1 --- Thresholding --- p.2-1 / Chapter 2.2 --- Edge Detection --- p.2-2 / Chapter 2.3 --- Region Growing --- p.2-2 / Chapter 2.4 --- Radial Contour Model --- p.2-3 / Chapter 2.5 --- Regularized Region Contrast --- p.2-3 / Chapter 2.6 --- Deformable Model --- p.2-4 / Chapter 3 --- The ODD-Balloons Model --- p.3-1 / Chapter 3.1 --- Design Rationale --- p.3-1 / Chapter 3.2 --- Overview --- p.3-5 / Chapter 3.3 --- 2-D Deformations --- p.3-8 / Chapter 3.4 --- Orthogonal Cut and Volume Transfer --- p.3-11 / Chapter 3.5 --- Smoothing Operation --- p.3-17 / Chapter 3.6 --- Properties --- p.3-20 / Chapter 3.6.1 --- Conformation to 3-D Shape --- p.3-20 / Chapter 3.6.2 --- Noise Sensitivity --- p.3-20 / Chapter 3.6.3 --- Convergence and Efficiency --- p.3-22 / Chapter 3.6.4 --- Easy-to-Use --- p.3-23 / Chapter 3.7 --- Summary --- p.3-24 / Chapter 4 --- Experiment Results --- p.4-1 / Chapter 4.1 --- Synthetic Data Experiments --- p.4-1 / Chapter 4.2 --- Real Data Experiment --- p.4-3 / Chapter 4.3 --- Discussions --- p.4-6 / Chapter 5 --- Conclusion and Future Work --- p.5-1 / Chapter 5.1 --- Conclusion --- p.5-1 / Chapter 5.2 --- Recommended Future Work --- p.5-2 / Appendix A Discrete Implementation of 2-D Deformation --- p.A-1 / Appendix B Choosing Elasticity and Rigidity Coefficients of 2-D Deformation --- p.B-1 / Bibliography --- p.BIB-1

Leung Yee Fong. / "August 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 262-287). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

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Diffusion-weighted image(DWI) and tractography have been developed for decades and are key elements in recent, large-scale efforts for mapping the human brain. The two techniques together provide us a unique possibility to access the macroscopic structure and connectivity of the human brain non-invasively and in vivo. The information obtained not only can help visualize brain connectivity and help segment the brain into different functional areas but also provides tools for understanding some major cognitive diseases such as multiple sclerosis, schizophrenia, epilepsy, etc. There are lots of efforts have been put into this area. On the one hand, a vast spectrum of tractography algorithms have been developed in recent years, ranging from deterministic approaches through probabilistic methods to global tractography; On the other hand, various mathematical models, such as diffusion tensor, multi-tensor model, spherical deconvolution, Q-ball modeling, have been developed to better exploit the acquisition dependent signal of Diffusion-weighted image(DWI). Despite considerable progress in this area, current methods still face many challenges, such as sensitive to noise, lots of false positive/negative fibers, incapable of handling complex fiber geometry and expensive computation cost. More importantly, recent researches have shown that, even with high-quality data, the results using current tractography methods may not be improved, suggesting that it is unlikely to obtain an anatomically accurate map of the human brain solely based on the diffusion profile. Motivated by these issues, this dissertation develops a global approach that incorporates anatomical validated geometric shape prior when reconstructing neuron fibers. The fiber tracts between regions of interest are initialized and updated via deformations based on gradients of the posterior energy defined in this paper. This energy has contributions from diffusion data, shape prior information, and roughness penalty. The dissertation first describes and demonstrates the proposed method on the 2D dataset and then extends it to 3D Phantom data and the real brain data. The results show that the proposed method is relatively immune to issues such as noise, complicated fiber structure like fiber crossings and kissing, false positive fibers, and achieve more explainable tractography results. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2019. / April 16, 2019. / active contours, Bayesian estimation, dMRI fiber tracts, geometric shape analysis, tractography / Includes bibliographical references. / Anuj Srivastava, Professor Directing Dissertation; Eric Klassen, University Representative; Wei Wu, Committee Member; Fred Huffer, Committee Member.

Bibliography: leaves 241-250. A multileaf collimator for radiation therapy has been designed that splits each leaf bank into two vertically displaced assemblies or levels with each level consisting of alternate leaves and leaf spaces. The radiation profiles transmitted for image formation through the collimator design were investigated to examine their dependence on the collimator design features.