Cervical Vertebral Corner Detection using Haar-like Features and Modified Hough Forest

Al Arifi, S.M.M.R., Asad, M., Knapp, K., Gundry, M. & Slabaugh, G.G. (2015). Cervical Vertebral Corner Detection using Haar-like Features and Modified Hough Forest. In: 2015 International Conference on Image Processing Theory, Tools and Applications. (pp. 417-422). IEEE. ISBN 978-1-4799-8636-1

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Abstract

The neck (cervical spine) is a flexible part of the human body and is particularly vulnerable to injury. Patients suspected of cervical spine injuries are often imaged using lateral view radiographs. Incorrect diagnosis based on these images may lead to serious long-term consequences. Our overarching goal is to develop a computer-aided detection system to help an emergency room physician correctly diagnose a patient's injury. In this paper, we present a method to localize the corners of cervical vertebrae in a set of 90 lateral cervical radiographs. Haar-like features are computed using intensity and gradient image patches, each of which votes for possible corner position using a modified Hough forest regression technique. Votes are aggregated using two dimensional kernel density estimation, to find the location of the corner. Our method demonstrates promising results, identifying corners with an average median error of 2.08 mm.

Item Type: Book Section
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords: Hough forest; random forest; classification; regression; cervical vertebrae; Haar-like features
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/17191

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