  Image segmentation in the medical domain is a challenging field owing to poor
resolution and limited contrast. The predominantly used conventional
segmentation techniques and the thresholding methods suffer from limitations
because of heavy dependence on user interactions. Uncertainties prevalent in an
image cannot be captured by these techniques. The performance further
deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy
C- means clustering for segmenting vertebral body from magnetic resonance image
owing to its unsupervised form of learning. The motivation for this work is
detection of spine geometry and proper localisation and labelling will enhance
the diagnostic output of a physician. The method is compared with Otsu
thresholding and K-means clustering to illustrate the robustness.The reference
standard for validation was the annotated images from the radiologist, and the
Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
