Performance Analysis of Distance Measures for Computer tomography Image Segmentation

This paper presents a comparative evaluation of different distance metrics for clustering data points for organ segmentation. Selecting the exact distance measure is the challenging problem in clustering. In this research work, we have compared Euclidean distance, Manhattan Distance, Minkowski distance, Chebyshev distance and Signature Quadratic form Distance measures. The main aim of this research work is to identify the best distance measures for exact segmentation of clustering the images by minimizing fragmentation issue. Real time Dataset are used to evaluate the distance measures.