A Hybrid K-Means Algorithm Improving Low-Density Map Based Medical Image Segmentation with Density Modification

Segmentation is grouping of a set of pixels, which are mapped from the structures inside the prostate and the background image. The main aim of this research is to provide a better segmentation technique for medical images by solving the drawbacks that currently exist in the density map-based discriminability of feature values. In this paper, we have proposed a method for image segmentation-based density map segmentation properties medical image. The accurateness of the resultant value possibly not up to the level of anticipation while the dimension of the dataset is high because we cannot say that the dataset chosen are free from noises and faults. The kernel change, i.e., segmentation is made by using hybrid K-means clustering algorithm. Thus this method is used to provide the segmentation processing information as well as also be noise free output in an efficient way. Hence, the developed model is implemented in the working platform of MATLAB and the output is compared with the existing techniques such as FCM, K-means to evaluate the performance of our proposed system.