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Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

Al-Arif, S. M., Gundry, M., Knapp, K. and Slabaugh, G.G. (2016). Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. Paper presented at the Computational Methods and Clinical Applications for Spine Imaging, 17 Oct 2016, Athens, Greece.

Abstract

X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: The final publication will be available at Springer via http://www.springer.com/gb/computer-science/lncs
Publisher Keywords: ASM, Classification forest, Cervical, Vertebrae, X-ray
Subjects: R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/15481
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