Virtual Colonoscopy and Abdominal Imaging Computational Challenges and Clinical Opportunities 1st Edition by Hiroyuki Yoshida, ‎Wenli Cai ISBN 9783642257186 by Wenli Cai, Hiroyuki Yoshida (auth.), Hiroyuki Yoshida, Wenli Cai (eds.) 9783642257186, 3642257186 instant download after payment.

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ISBN 13: 9783642257186
Author: Hiroyuki Yoshida, ‎Wenli Cai

This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, held in conjunction with MICCAI 2010, in Beijing, China, on September 20, 2010. The 19 revised full papers presented were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on CT colonography CAD, abdominal imaging, and virtual colonoscopy

Virtual Colonoscopy and Abdominal Imaging Computational Challenges and Clinical Opportunities 1st Table of contents:

1. CT Colonography CAD
2. Electronic Cleansing in CT Colonography: Past, Present, and Future
3. Introduction
4. Fecal-Tagging CTC
5. Early Work of EC
6. Current Status of EC
7. Future Challenges of EC
8. Conclusion
9. References
10. Improved Curvature Estimation for Shape Analysis in Computer-Aided Detection of Colonic Polyps
11. Purpose
12. Methods
13. Kernel Methods for Principal Curvature Estimation
14. Knutsson Mapping Method
15. Results
16. Conclusion
17. References
18. Characterizing Colonic Detections in CT Colonography Using Curvature-Based Feature Descriptor and Ba
19. Introduction
20. Methods
21. Curvature-Based Feature Descriptor
22. Bag-of-Words (BoW) Model
23. Content-Based Image Retrieval (CBIR)
24. Experiments and Results
25. Phantom Experiments
26. CTC Experiments
27. Discussion and Conclusion
28. References
29. Haustral Fold Segmentation of CT Colonography Using Ridge Line Detection
30. Purpose
31. Methods
32. Colon Surface Extraction
33. Ridge Line Detection
34. Fold Segmentation
35. Results
36. Conclusion
37. References
38. Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonograp
39. Introduction
40. Classes of FP Reduction Techniques
41. Feature-Based Classifiers
42. Pixel-Based Machine Learning (PML)
43. Non-machine-Learning-Based Methods
44. Reduction of Specific Type of FP
45. FP Reduction with MTANNs
46. CTC Database
47. Evaluation
48. Conclusion
49. References
50. A Bayesian Approach for False Positive Reduction in CTC CAD
51. Introduction
52. Method
53. Modeling the Likelihood Term
54. Experimental Results and Discussion
55. Conclusion
56. References
57. False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography: A Massive-Trainin
58. Introduction
59. Massive-Training Support Vector Regression (MTSVR)
60. Architecture and Training of MTSVR
61. Support Vector Regression (SVR)
62. CTC Database and Evaluation
63. Results
64. Conclusion
65. References
66. Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model
67. Introduction
68. Method
69. Overview
70. Probabilistic Models for 2D Candidate Detection of RT Regions
71. RT Path Estimation Using a Global Shape Model
72. Results
73. Discussion and Conclusion
74. References
75. Abdominal Imaging
76. Estimation of Necrosis Volumes in Focal Liver Lesions Based on Multi-phase Hepatic CT Images
77. Introduction
78. Methods
79. Registration
80. Blood-Flow Estimation
81. Segmentation
82. Results
83. Conclusion
84. References
85. Detection of the Invasion of Bladder Tumor into Adjacent Wall Based on Textural Features Extracted f
86. Introduction
87. Materials and Method
88. Subjects and MRI Datasets
89. Overview of the Detection Scheme
90. Selection of Textural Features
91. Extraction of Textural Features
92. Training and Evaluation of the SVM Classifier
93. Tissue Labeling for the Detection of Invasion Depth
94. Results
95. Conclusions
96. References
97. Detecting Bladder Abnormalities Based on Inter-layer Intensity Curve for Virtual Cystoscopy
98. Introduction
99. Methods
100. Bladder Wall Segmentation
101. Bladder Wall Layer Generation
102. ILIC Generation
103. Result and Discussion
104. Conclusion
105. References
106. Computer-Assisted Diagnosis for Quantitative Image-Based Analysis of Crohn’s Disease in CT Enterog
107. Introduction
108. Method
109. Clinical CT Enterography Cases
110. CADx Scheme
111. Evaluation Methodology
112. Results
113. Conclusion
114. References
115. Computer-Aided Detection of Small Bowel Strictures for Emergency Radiology in CT Enterography
116. Introduction
117. Method
118. CAD Scheme
119. Clinical CT Enterography Cases
120. Results
121. Image Analysis by an Experienced Radiologist without CAD
122. Image Analysis by an Inexperienced Radiologist Assisted by CAD
123. Comparison of Readers’ Results with and without CAD
124. Conclusion
125. References
126. Virtual Colonoscopy
127. Teniae Coli Extraction in Human Colon for Computed Tomographic Colonography Images
128. Introduction
129. Colon Unfolding
130. Teniae Coli Extraction for CT Colonography Images
131. Gabor Filter for Fold Feature Extraction
132. Identifying Center of Haustral Folds by Use of Thresholding
133. Extraction of Teniae Coli
134. Experimental Results
135. Conclusion and Future Work
136. References
137. Extraction of Landmarks and Features from Virtual Colon Models
138. Introduction
139. Extraction of Anatomic Landmarks
140. Detection of Features
141. Conclusion
142. References
143. Conformal Geometry Based Supine and Prone Colon Registration
144. Introduction
145. Algorithm Overview
146. Conformal Mapping
147. Registration by Harmonic Map
148. Experimental Results
149. Conclusion
150. References
151. Colon Visualization Using Shape Preserving Flattening
152. Introduction
153. Generating 2D Views
154. Integration for 3D Navigation
155. Conclusion
156. References
157. Synchronized Display of Virtual Colonoscopic Views in Supine and Prone CT Images
158. Introduction
159. Synchronized Supine-Prone Views
160. Overview
161. Supine-Prone Correspondence Finding
162. Viewpoint Synchronization
163. Calculation of Viewing-Direction and Up-Direction
164. Experiments
165. Duscussion
166. Conclusion
167. References
168. Colorectal Polyp Segmentation Based on Geodesic Active Contours with a Shape-Prior Model
169. Introduction
170. Method
171. Overview
172. Construction of the Shape-Prior Model
173. Polyp Segmentation
174. Experiments and Results
175. Discussion and Conclusions

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Tags: Hiroyuki Yoshida, ‎Wenli Cai, Colonoscopy, Abdominal