Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching

Mona Sharifi Sarabi; Diane Villaroman; Joel Beckett; Mark Attiah; Logan Marcus; Christine Ahn; Diana Babayan; Bilwaj Gaonkar; Luke Macyszyn; Cauligi Raghavendra

doi:10.1117/12.2254582

3 March 2017 Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching

Mona Sharifi Sarabi, Diane Villaroman, Joel Beckett, Mark Attiah, Logan Marcus, Christine Ahn, Diana Babayan, Bilwaj Gaonkar, Luke Macyszyn, Cauligi Raghavendra

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013447 (2017) https://doi.org/10.1117/12.2254582
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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].

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Mona Sharifi Sarabi, Diane Villaroman, Joel Beckett, Mark Attiah, Logan Marcus, Christine Ahn, Diana Babayan, Bilwaj Gaonkar, Luke Macyszyn, and Cauligi Raghavendra "Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013447 (3 March 2017); https://doi.org/10.1117/12.2254582

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KEYWORDS

X-rays

Pathology

Computed tomography

Image segmentation

Magnetic resonance imaging

Spine

X-ray imaging

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