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.

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