Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

Chen, Cheng; Zheng, Guoyan (2013). Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features. In: Kamel, Mohamed; Campilho, Aurelio (eds.) 10th International Conference, ICIAR 2013, Proceedings. Lecture Notes in Computer Science: Vol. 7950 (pp. 442-450). Springer 10.1007/978-3-642-39094-4_50

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In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Chen, Cheng and Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISBN:

978-3-642-39093-7

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Guoyan Zheng

Date Deposited:

12 Jun 2014 16:16

Last Modified:

12 Jun 2014 16:16

Publisher DOI:

10.1007/978-3-642-39094-4_50

URI:

https://boris.unibe.ch/id/eprint/46492

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