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Proceedings Paper

Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching
Author(s): Mona Sharifi Sarabi; Diane Villaroman; Joel Beckett; Mark Attiah; Logan Marcus; Christine Ahn; Diana Babayan; Bilwaj Gaonkar; Luke Macyszyn; Cauligi Raghavendra
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Paper Abstract

Lower back pain and pathologies related to it are one of the most common results for a referral to a neurosurgical clinic in the developed and the developing world. Quantitative evaluation of these pathologies is a challenge. Image based measurements of angles/vertebral heights and disks could provide a potential quantitative biomarker for tracking and measuring these pathologies. Detection of vertebral bodies is a key element and is the focus of the current work. From the variety of medical imaging techniques, MRI and CT scans have been typically used for developing image segmentation methods. However, CT scans are known to give a large dose of x-rays, increasing cancer risk [8]. MRI can be substituted for CTs when the risk is high [8] but are difficult to obtain in smaller facilities due to cost and lack of expertise in the field [2]. X-rays provide another option with its ability to control the x-ray dosage, especially for young people, and its accessibility for smaller facilities. Hence, the ability to create quantitative biomarkers from x-ray data is especially valuable. Here, we develop a multiscale template matching, inspired by [9], to detect centers of vertebral bodies from x-ray data. The immediate application of such detection lies in developing quantitative biomarkers and in querying similar images in a database. Previously, shape similarity classification methods have been used to address this problem, but these are challenging to use in the presence of variation due to gross pathology and even subtle effects [1].

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013447 (3 March 2017); doi: 10.1117/12.2254582
Show Author Affiliations
Mona Sharifi Sarabi, The Univ. of Southern California (United States)
Diane Villaroman, Univ. of California, Los Angeles (United States)
Joel Beckett, Univ. of California, Los Angeles (United States)
Mark Attiah, Univ. of California, Los Angeles (United States)
Logan Marcus, Univ. of California, Los Angeles (United States)
Christine Ahn, Univ. of California, Los Angeles (United States)
Diana Babayan, Univ. of California, Los Angeles (United States)
Bilwaj Gaonkar, Univ. of California, Los Angeles (United States)
Luke Macyszyn, Univ. of California, Los Angeles (United States)
Cauligi Raghavendra, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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