Coarse-to-fine localization of anatomical landmarks in CT images based on multi-scale local appearance and rotation-invariant spatial landmark distribution model

Mitsutaka Nemoto; Yoshitaka Masutani; Shouhei Hanaoka; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa; Naoto Hayashi; Kuni Ohtomo

doi:10.1117/12.2007134

13 March 2013 Coarse-to-fine localization of anatomical landmarks in CT images based on multi-scale local appearance and rotation-invariant spatial landmark distribution model

Mitsutaka Nemoto, Yoshitaka Masutani, Shouhei Hanaoka, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Kuni Ohtomo

Author Affiliations +

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866917 (2013) https://doi.org/10.1117/12.2007134
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Mitsutaka Nemoto, Yoshitaka Masutani, Shouhei Hanaoka, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, and Kuni Ohtomo "Coarse-to-fine localization of anatomical landmarks in CT images based on multi-scale local appearance and rotation-invariant spatial landmark distribution model", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866917 (13 March 2013); https://doi.org/10.1117/12.2007134

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KEYWORDS

Computed tomography

Medical imaging

Error analysis

Statistical analysis

Image segmentation

Image processing

Feature extraction

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