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Proceedings Paper

A unified framework for concurrent detection of anatomical landmarks for medical image understanding
Author(s): Mitsutaka Nemoto; Yoshitaka Masutani; Shouhei Hanaoka; Yukihiro Nomura; Takeharu Yoshikawa; Naoto Hayashi; Naoki Yoshioka; Kuni Ohtomo
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Paper Abstract

Anatomical landmarks are useful as the primitive anatomical knowledge for medical image understanding. In this study, we construct a unified framework for automated detection of anatomical landmarks distributed within the human body. Our framework includes the following three elements; (1) initial candidate detection based on a local appearance matching technique based on appearance models built by PCA and the generative learning, (2) false positive elimination using classifier ensembles trained by MadaBoost, and (3) final landmark set determination based on a combination optimization method by Gibbs sampling with a priori knowledge of inter-landmark distances. In evaluation of our methods with 50 data sets of body trunk CT, the average sensitivity in detecting candidates of 165 landmarks was 0.948 ± 0.084 while 55 landmarks were detected with 100 % sensitivity. Initially, the amount of false positives per landmark was 462.2 ± 865.1 per case on average, then they were reduced to 152.8 ± 363.9 per case by the MadaBoost classifier ensembles without miss-elimination of the true landmarks. Finally 89.1 % of landmarks were correctly selected by the final combination optimization. These results showed that our framework is promising for an initial step for the subsequent anatomical structure recognition.

Paper Details

Date Published: 14 March 2011
PDF: 13 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623E (14 March 2011); doi: 10.1117/12.878327
Show Author Affiliations
Mitsutaka Nemoto, The Univ. of Tokyo (Japan)
Yoshitaka Masutani, The Univ. of Tokyo (Japan)
Shouhei Hanaoka, The Univ. of Tokyo (Japan)
The Health and Life Science Univ. Hall in Tirol (Austria)
Yukihiro Nomura, The Univ. of Tokyo (Japan)
Takeharu Yoshikawa, The Univ. of Tokyo (Japan)
Naoto Hayashi, The Univ. of Tokyo (Japan)
Naoki Yoshioka, The Univ. of Tokyo (Japan)
Kuni Ohtomo, The Univ. of Tokyo (Japan)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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