Preliminary study on appearance-based detection of anatomical point landmarks in body trunk CT images

Authors:
Mitsutaka Nemoto;Yukihiro Nomura;Shohei Hanaoka;Yoshitaka Masutani;Takeharu Yoshikawa;Naoto Hayashi;Naoki Yoshioka;Kuni Ohtomo
Affiliations:
Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan and Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan and Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan;Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan and Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Venue:
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Year:
2010

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Abstract

Anatomical point landmarks as most primitive anatomical knowledge are useful for medical image understanding. In this study, we propose a detection method for anatomical point landmark based on appearance models, which include gray-level statistical variations at point landmarks and their surrounding area. The models are built based on results of Principal Component Analysis (PCA) of sample data sets. In addition, we employed generative learning method by transforming ROI of sample data. In this study, we evaluated our method with 24 data sets of body trunk CT images and obtained 95.8 ± 7.3 % of the average sensitivity in 28 landmarks.