Paraspinal Muscle Segmentation in CT Images Using GSM-Based Fuzzy C-Means Clustering

Yong Wei, Xiuping Tao, Bin Xu, Arend P. Castelein

Department of Chemistry, Winston-Salem State University, Winston-Salem, USA.
Department of Computer Science and Information Systems, University of North Georgia, Dahlonega, USA.
Department of Spine Surgery, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, China.

DOI: 10.4236/jcc.2014.29010                

Abstract

Minimally Invasive Spine surgery (MISS) was developed to treat disorders of the spine with less disruption to the muscles. Surgeons use CT images to monitor the volume of muscles after operation in order to evaluate the progress of patient recovery. The first step in the task is to segment the muscle regions from other tissues/organs in CT images. However, manual segmentation of muscle regions is not only inaccurate, but also time consuming. In this work, Gray Space Map (GSM) is used in fuzzy c-means clustering algorithm to segment muscle regions in CT images. GSM com- bines both spatial and intensity information of pixels. Experiments show that the proposed GSM- based fuzzy c-means clustering muscle CT image segmentation yields very good results.

Keywords

CT Image, Segmentation, Gray Space Map (GSM), Fuzzy C-Means Clustering, Minimally Invasive Spine Surgery (MISS)

Conflicts of Interest

The authors declare no conflicts of interest.

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