Unsupervised Medical Image Classification Based on Skew Gaussian Mixture Model and Hierarchical Clustering Algorithm

• Published in: Advances in Digital Image Processing and Information Technology pp. 65 - 74
• Main Authors: Vadaparthi, Nagesh, Yarramalle, Srinivas, Suresh Varma, P.
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011
• Series: Communications in Computer and Information Science
• Subjects: Classification; finite Gaussian mixture model; image quality metrics; Segmentation; Skew Gaussian distribution
• ISBN: 9783642240546; 3642240542
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-642-24055-3_7

A novel segmentation algorithm for brain images is proposed using finite skew Gaussian mixture model. Recently, much work has been reported in medical image segmentation. Among these techniques, finite Gaussian mixture models are considered to be more recent and accurate. However, in this approach, a number of segments that an image can be divided are taken through apriori and if these segments are not initiated properly it leads to misclassification. Hence, to overcome this disadvantage, we proposed an algorithm for Medical Image Segmentation using Hierarchical Clustering and Skew Gaussian Mixture. The experimentation is done with four different brain images and the results obtained are evaluated using Quality metrics.