Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Dong, Xiao; Zheng, Guoyan (2014). Graphical Model-Based Vertebra Identification from X-Ray Image(s). In: Li, Shuo; Yao, Jianhua (eds.) Spinal Imaging and Image Analysis. Lecture Notes in Computational Vision and Biomechanics: Vol. 18 (pp. 367-379). Cham: Springer International Publishing 10.1007/978-3-319-12508-4_12

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Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

2212-9391

ISBN:

978-3-319-12507-7

Series:

Lecture Notes in Computational Vision and Biomechanics

Publisher:

Springer International Publishing

Language:

English

Submitter:

Guoyan Zheng

Date Deposited:

01 May 2015 15:53

Last Modified:

10 Feb 2016 04:50

Publisher DOI:

10.1007/978-3-319-12508-4_12

BORIS DOI:

10.7892/boris.67978

URI:

https://boris.unibe.ch/id/eprint/67978

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