A novel approach for medical image segmentation using PCA and K-means clustering

Physician use medical images to find abnormalities in human bodies and to locate the discontinuities. In transmission process sometimes medical images are corrupted due to external noise. Therefore need to improve the image quality, put down the computational complexity and signal to noise ratio. There is need of segmentation to improve performance analysis and image quality. Segmentation method is use to identify important regions in medical images, it is a unique technique for partitioning image into meaningful sub regions or object with same attribute. Proposed method state Principle component analysis and k-means clustering method for segmentation of medical images and extracts meaningful part from medical image in efficient manner. K-means Clustering is the process of extracting meaningful part from image. Adding Principle Component Analysis for feature extraction and formation of precise number of cluster to increase the accuracy. To develop a system which will perform segmentation of MRI images to locate disorder in better way.