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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. (2017). Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B. and Li, S. (Eds.), Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. Lecture Notes in Computer Science, 10182. (pp. 3-15). Cham: Springer. ISBN 978-3-319-55049-7

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: Book Section
Additional Information: The final authenticated version is available online at https://doi.org/10.1007/978-3-319-55050-3_1
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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