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Image segmentation in the medical domain is a challenging field owing to poorresolution and limited contrast. The predominantly used conventionalsegmentation techniques and the thresholding methods suffer from limitationsbecause of heavy dependence on user interactions. Uncertainties prevalent in animage cannot be captured by these techniques. The performance furtherdeteriorates when the images are corrupted by noise, outliers and otherartifacts. The objective of this paper is to develop an effective robust fuzzyC- means clustering for segmenting vertebral body from magnetic resonance imageowing to its unsupervised form of learning. The motivation for this work isdetection of spine geometry and proper localisation and labelling will enhancethe diagnostic output of a physician. The method is compared with Otsuthresholding and K-means clustering to illustrate the robustness.The referencestandard for validation was the annotated images from the radiologist, and theDice coefficient and Hausdorff distance measures were used to evaluate thesegmentation.

Fuzzy Clustering Based Segmentation Of Vertebrae in T1-Weighted Spinal MR Images