Paper Abstract and Keywords |
Presentation |
2011-11-29 10:50
Statistical modeling of spacial landmark distribution: model building techniques for datasets with various imaging ranges Shouhei Hanaoka, Yoshitaka Masutani, Mitsutaka Nemoto, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Kuni Ohtomo (Univ. of Tokyo) MI2011-67 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In building a statistical shape model, each training dataset is implicitly required to have the enough imaging range which includes all organs of interest. However, this assumption may not be satisfied in modeling of the whole human body. In this study, we developed a novel method to build a special landmark distribution model from image datasets with partial and various imaging ranges. The EM algorithm was utilized to estimate the missing data in the given training datasets. The proposed algorithm was evaluated with 40 whole-torso CT datasets. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Anatomical landmark / CT / statistical shape model / EM algorithm / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 111, no. 331, MI2011-67, pp. 25-30, Nov. 2011. |
Paper # |
MI2011-67 |
Date of Issue |
2011-11-22 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2011-67 |
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