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

Abstract

Abstract.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.

A Hybrid of NHPP and Generalized Gaussian Mixture Model: A Combinatorial Approach for Background Elimination

“…NHPP-based SGRM might be applicable to all observed failure data if the right averaging function is chosen. The mean function and the NHPP-based SRGM are identical [2,[14][15][16][17]]. An early SRGM built on the NHPP is the Zhao and Xie model [18].…”

An Improved Brain Mr Image Segmentation using Truncated Skew Gaussian Mixture

“…But in reality, white matter regions contain certain portion of grey matter at the boundaries and the tissues within these regions are assumed to contain the pixels having the probabilities which may be both symmetric and non-symmetric [5]. The problem gets multifold in case of abnormal brains, since registering these images with prior probabilities is difficult, as each pixel inside a region may belongs to a different class.…”

“…Among these techniques, medical image segmentation based on K-Means is mostly utilized [4]. But, the main disadvantage with K-Means is that, K-Means are slow in convergence and pseudo unsupervised learning that requires the initial value of K. Apart from K-Means, hierarchical clustering algorithm [5] is also used but even this algorithm shares similar arguments as the case of K-Means algorithm. Fuzzy C-Means clustering algorithm in considered, in order to identify the initial clusters.…”