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Zhao, Y.Q., Zan, Y.L., Wang, X.F. and Li, G.Y. (2010) Fuzzy C-means Clustering-Based Multilayer Perception Neu- ral Network for Liver CT Images Automatic Segmentation. Control and Decision Conference (CCDC), Xuzhou, May 2010, 3423-3427.

has been cited by the following article:

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

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

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

  JOURNAL NAME: Journal of Computer and Communications, Vol.2 No.9, July 11, 2014

  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.