Jiyo.S.Athertya and G.Saravana Kumar, IIT-Madras, India

Computerized medical image segmentation is a challenging area because of poor resolution and weak contrast. The predominantly used conventional clustering techniques and the thresholding methods suffer from limitations owing to their 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 images. The motivation for this work is that spine appearance, shape and geometry measurements are necessary for abnormality detection and thus 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.

Vertebra segmentation, fuzzy clustering, MRI, labelling