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Proceedings Paper

Coarse-to-fine localization of anatomical landmarks in CT images based on multi-scale local appearance and rotation-invariant spatial landmark distribution model
Author(s): Mitsutaka Nemoto; Yoshitaka Masutani; Shouhei Hanaoka; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa; Naoto Hayashi; Kuni Ohtomo
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Paper Abstract

The detection of anatomical landmarks (LMs) often plays a key role in medical image analysis. In our previous study, we reported an automatic LM detection method for CT images. Despite its high detection sensitivity, the distance errors of the detection results for some LMs were relatively large as they sometimes exceeded 10 mm. Naturally, it is desirable to minimize LM detection error, especially when the LM detection results are used in image analysis tasks such as image segmentation. In this study, we introduce a novel method of coarse-to-fine localization to increase accuracy, which refines the LM positions detected by our previous method. The proposed LM localization is performed by both multiscale local image pattern recognition and likelihood estimation from prior knowledge of the spatial distribution of multiple LMs. Classifier ensembles for recognizing local image patterns are trained by the cost-sensitive MadaBoost. The cost of each sample is altered depending on its distance from the ground truth LM position. The spatial LM distribution likelihood, calculated from a statistical model of inter-landmark distances between all LM pairs, is also used in the localization. The evaluation experiment was performed with 15 LMs in 39 CT images. The average distance error of the pre-detected LM position was improved by 2.05 mm by the proposed localization method. The proposed method was shown to be effective for reducing LM detection error.

Paper Details

Date Published: 13 March 2013
PDF: 9 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866917 (13 March 2013); doi: 10.1117/12.2007134
Show Author Affiliations
Mitsutaka Nemoto, The Univ. of Tokyo Hospital (Japan)
Yoshitaka Masutani, The Univ. of Tokyo Hospital (Japan)
The Univ. of Tokyo (Japan)
Shouhei Hanaoka, The Univ. of Tokyo Hospital (Japan)
Yukihiro Nomura, The Univ. of Tokyo Hospital (Japan)
Soichiro Miki, The Univ. of Tokyo (Japan)
Takeharu Yoshikawa, The Univ. of Tokyo Hospital (Japan)
Naoto Hayashi, The Univ. of Tokyo Hospital (Japan)
Kuni Ohtomo, The Univ. of Tokyo Hospital (Japan)
The Univ. of Tokyo (Japan)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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