Thesis (M.S.)--Case Western Reserve University, 2008. / [School of Medicine] Department of Clinical Research. Includes bibliographical references.

Thesis (Ph. D.)--Stanford University, 1996. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.A.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Mar. 18, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Arts, [Department of] Humanities Computing, University of Alberta. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2000. / Vita. Includes bibliographical references (leaves 661-707).

Thesis (Phd. (South African National Bioinformatics Institute, Faculty of Natural Sciences))--University of the Western Cape, 2009. / Includes bibliographical references (104-112) and index.

Thesis (Ph. D.)--Stanford University, 1996. / Includes bibliographical references.

Thesis (M.S.)--Georgia State University, 2007. / Title from title page. Yu-sheng Hsu, committee chair; Xu Zhang, Jia-wei Liu, committee members. Electronic text (44 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Oct. 10, 2007. Includes bibliographical references (p. 35-36).

Thesis (Ph.D.)--Drexel University, 2008. / Includes abstract and vita. Includes bibliographical references (leaves 147-153).

Lee Kit Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 115-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Project Background --- p.1 / Chapter 1.2 --- Problem Specifications --- p.3 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Medical Data Mining --- p.8 / Chapter 2.1.1 --- General Information --- p.9 / Chapter 2.1.2 --- Related Research --- p.10 / Chapter 2.1.3 --- Characteristics and Difficulties Encountered --- p.11 / Chapter 2.2 --- DNA Sequence Analysis --- p.13 / Chapter 2.3 --- Hepatitis B Virus --- p.14 / Chapter 2.3.1 --- Virus Characteristics --- p.15 / Chapter 2.3.2 --- Important Findings on the Virus --- p.17 / Chapter 2.4 --- Bayesian Network and its Classifiers --- p.17 / Chapter 2.4.1 --- Formal Definition --- p.18 / Chapter 2.4.2 --- Existing Learning Algorithms --- p.19 / Chapter 2.4.3 --- Evolutionary Algorithms and Hybrid EP (HEP) --- p.22 / Chapter 2.4.4 --- Bayesian Network Classifiers --- p.25 / Chapter 2.4.5 --- Learning Algorithms for BN Classifiers --- p.32 / Chapter 3 --- Bayesian Network Classifier for Clinical Data --- p.35 / Chapter 3.1 --- Related Work --- p.36 / Chapter 3.2 --- Proposed BN-augmented Naive Bayes Classifier (BAN) --- p.38 / Chapter 3.2.1 --- Definition --- p.38 / Chapter 3.2.2 --- Learning Algorithm with HEP --- p.39 / Chapter 3.2.3 --- Modifications on HEP --- p.39 / Chapter 3.3 --- Proposed General Bayesian Network with Markov Blan- ket (GBN) --- p.40 / Chapter 3.3.1 --- Definition --- p.41 / Chapter 3.3.2 --- Learning Algorithm with HEP --- p.41 / Chapter 3.4 --- Findings on Bayesian Network Parameters Calculation --- p.43 / Chapter 3.4.1 --- Situation and Errors --- p.43 / Chapter 3.4.2 --- Proposed Solution --- p.46 / Chapter 3.5 --- Performance Analysis on Proposed BN Classifier Learn- ing Algorithms --- p.47 / Chapter 3.5.1 --- Experimental Methodology --- p.47 / Chapter 3.5.2 --- Benchmark Data --- p.48 / Chapter 3.5.3 --- Clinical Data --- p.50 / Chapter 3.5.4 --- Discussion --- p.55 / Chapter 3.6 --- Summary --- p.56 / Chapter 4 --- Classification in DNA Analysis --- p.57 / Chapter 4.1 --- Related Work --- p.58 / Chapter 4.2 --- Problem Definition --- p.59 / Chapter 4.3 --- Proposed Methodology Architecture --- p.60 / Chapter 4.3.1 --- Overall Design --- p.60 / Chapter 4.3.2 --- Important Components --- p.62 / Chapter 4.4 --- Clustering --- p.63 / Chapter 4.5 --- Feature Selection Algorithms --- p.65 / Chapter 4.5.1 --- Information Gain --- p.66 / Chapter 4.5.2 --- Other Approaches --- p.67 / Chapter 4.6 --- Classification Algorithms --- p.67 / Chapter 4.6.1 --- Naive Bayes Classifier --- p.68 / Chapter 4.6.2 --- Decision Tree --- p.68 / Chapter 4.6.3 --- Neural Networks --- p.68 / Chapter 4.6.4 --- Other Approaches --- p.69 / Chapter 4.7 --- Important Points on Evaluation --- p.69 / Chapter 4.7.1 --- Errors --- p.70 / Chapter 4.7.2 --- Independent Test --- p.70 / Chapter 4.8 --- Performance Analysis on Classification of DNA Data --- p.71 / Chapter 4.8.1 --- Experimental Methodology --- p.71 / Chapter 4.8.2 --- Using Naive-Bayes Classifier --- p.73 / Chapter 4.8.3 --- Using Decision Tree --- p.73 / Chapter 4.8.4 --- Using Neural Network --- p.74 / Chapter 4.8.5 --- Discussion --- p.76 / Chapter 4.9 --- Summary --- p.77 / Chapter 5 --- Adaptive HEP for Learning Bayesian Network Struc- ture --- p.78 / Chapter 5.1 --- Background --- p.79 / Chapter 5.1.1 --- Objective --- p.79 / Chapter 5.1.2 --- Related Work - AEGA --- p.79 / Chapter 5.2 --- Feasibility Study --- p.80 / Chapter 5.3 --- Proposed A-HEP Algorithm --- p.82 / Chapter 5.3.1 --- Structural Dissimilarity Comparison --- p.82 / Chapter 5.3.2 --- Dynamic Population Size --- p.83 / Chapter 5.4 --- Evaluation on Proposed Algorithm --- p.88 / Chapter 5.4.1 --- Experimental Methodology --- p.89 / Chapter 5.4.2 --- Comparison on Running Time --- p.93 / Chapter 5.4.3 --- Comparison on Fitness of Final Network --- p.94 / Chapter 5.4.4 --- Comparison on Similarity to the Original Network --- p.95 / Chapter 5.4.5 --- Parameter Study --- p.96 / Chapter 5.5 --- Applications on Medical Domain --- p.100 / Chapter 5.5.1 --- Discussion --- p.100 / Chapter 5.5.2 --- An Example --- p.101 / Chapter 5.6 --- Summary --- p.105 / Chapter 6 --- Conclusion --- p.107 / Chapter 6.1 --- Summary --- p.107 / Chapter 6.2 --- Future Work --- p.109 / Bibliography --- p.117

書名中的[hu], 字形為: '點'在上, '尸'在下. / Shu ming zhong de [hu], zi xing wei: 'dian' zai shang, 'shi' zai xia. / Meng, Qiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 92-99). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- User Interaction in Medical Applications --- p.1 / Chapter 1.2 --- UI Technologies and Challenges for Medical Systems --- p.2 / Chapter 1.3 --- Main Contributions of the Thesis --- p.5 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Interactive Vascular Designing and Modeling --- p.9 / Chapter 2.1 --- Introduction and Related Works --- p.10 / Chapter 2.2 --- Vascular Designing and Modeling System Overview --- p.12 / Chapter 2.3 --- Data Structure for Vascular Tree --- p.13 / Chapter 2.4 --- VesselEdit 一 A Freehand Vessel Skeleton Generator --- p.17 / Chapter 2.4.1 --- 2D scribble to create 3D vessel tree --- p.17 / Chapter 2.4.2 --- 3D Skeleton Editing --- p.18 / Chapter 2.5 --- Feature Point Selection and Spline Segment Construction --- p.18 / Chapter 2.5.1 --- Feature Point Update --- p.18 / Chapter 2.5.2 --- Feature Point Selection --- p.20 / Chapter 2.5.3 --- Spline Segment Construction --- p.20 / Chapter 2.6 --- Vascular Tree Visualization --- p.22 / Chapter 2.6.1 --- Curve Frame --- p.22 / Chapter 2.6.2 --- Bifurcation Frame --- p.24 / Chapter 2.6.3 --- Frame Junction and Blending --- p.25 / Chapter 2.6.4 --- Transparency Enhancement --- p.27 / Chapter 2.7 --- Modeling Case Study and Results --- p.28 / Chapter 2.7.1 --- Normal cases --- p.28 / Chapter 2.7.2 --- Pathological Cases for Vascular Interventional Simulation --- p.28 / Chapter 2.7.3 --- Timing Experiments --- p.30 / Chapter 3 --- Vascular Intervention Simulator System --- p.32 / Chapter 3.1 --- Introduction to Vascular Intervention Simulator --- p.33 / Chapter 3.2 --- Overview of the endovascSim System --- p.34 / Chapter 3.3 --- Guidewire Sensing Hardware Interface Design --- p.36 / Chapter 3.3.1 --- Catheter & Guidewire Motion Sensing Requirements --- p.36 / Chapter 3.3.2 --- Motion Sensing with Trackball Mouse --- p.38 / Chapter 3.3.3 --- Multi-Mouse Device for Catheter & Guidewire Motion Sens- ing --- p.39 / Chapter 4 --- User Interaction for Visible Human Slice Navigation --- p.42 / Chapter 4.1 --- Introduction and Related Works --- p.43 / Chapter 4.2 --- VH Slice Navigation System Overview --- p.44 / Chapter 4.3 --- VH Data Compression --- p.45 / Chapter 4.3.1 --- VH Data Down Sampling --- p.46 / Chapter 4.3.2 --- Bounding Box Compression --- p.47 / Chapter 4.3.3 --- DXT Compression --- p.51 / Chapter 4.3.4 --- Compressed Visible Human Data Format --- p.53 / Chapter 4.4 --- Slice Pixels Calculation --- p.55 / Chapter 4.4.1 --- Pixels Color Computation --- p.55 / Chapter 4.4.2 --- CPU-GPU Cooperative Computation Framework --- p.58 / Chapter 4.4.3 --- CPU-GPU Computation Balancing Method --- p.60 / Chapter 4.5 --- User Interaction Design --- p.63 / Chapter 4.5.1 --- Slice navigation and haptic rendering --- p.64 / Chapter 4.5.2 --- Software UI layout and slice bookmarking --- p.66 / Chapter 4.6 --- System Implementation and Experimental Result --- p.68 / Chapter 5 --- Volume Data Exploration with Tangible Handheld Device --- p.71 / Chapter 5.1 --- Introduction and Related Works --- p.72 / Chapter 5.1.1 --- Introduction to Our Exploration System --- p.72 / Chapter 5.1.2 --- Ralated Works --- p.74 / Chapter 5.2 --- System Overview --- p.75 / Chapter 5.2.1 --- Hardware --- p.76 / Chapter 5.2.2 --- Server Program --- p.77 / Chapter 5.2.3 --- Client Program --- p.78 / Chapter 5.3 --- "Volumetric Data, Exploration and Annotation" --- p.78 / Chapter 5.3.1 --- Volume Data Manipulation --- p.79 / Chapter 5.3.2 --- "Volume Data, Slicing" --- p.80 / Chapter 5.3.3 --- "Volume Data, Visual Annotation" --- p.82 / Chapter 5.3.4 --- Volume Data Measurement --- p.84 / Chapter 6 --- Conclusion and Future Directions --- p.86 / Chapter 6.1 --- Conclusion --- p.86 / Chapter 6.2 --- Future Works --- p.88 / Publication List --- p.90 / Bibliography --- p.92