Morphological image processing approach on the detection of tumor and cancer cells

Image processing is one of most growing research area these days and now it is very much integrated with the medical and biotechnology field. Image Processing can be used to analyze different medical and MRI images to get the abnormality in the image. This paper proposes an efficient K-means clustering algorithm under Morphological Image Processing (MIP). Medical Image segmentation deals with segmentation of tumor in CT and MR images for improved quality in medical diagnosis. It is an important process and a challenging problem due to noise presence in input images during image analysis. It is needed for applications involving estimation of the boundary of an object, classification of tissue abnormalities, shape analysis, contour detection. Segmentation determines as the process of dividing an image into disjoint homogeneous regions of a medical image. The amount of resources required to describe large set of data is simplified and is selected for tissue segmentation. In our paper, this segmentation is carried out using K-means clustering algorithm for better performance. This enhances the tumor boundaries more and is very fast when compared to many other clustering algorithms. This paper produces the reliable results that are less sensitive to error.