Special Issue Article Open Access

Tumor cut segmentation for Blemish Cells Detection in Human Brain Based on Cellular Automata

Abstract

Image Processing is one of the emergent research areas today. Medical image processing is the most challenging and highly wanted field in that. Brain tumor detection in Magnetic resonance imaging (MRI) has become an emergent area in the field of medical image processing. We present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radio surgery planning and assessment of the response to the therapy. In this paper the brain tumor is detected using image segmentation process. Defining in simple terms Segmentation refers to the procedure of partitioning a digital image into various sectors. The purpose of segmentation is to shorten or modify the representation of an image into something that is more significant and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. Image segmentation is the process of dividing an image into different homogeneous regions.MR Image segmentation is done through clustering. Clustering is a method of grouping a set of patterns into a number of clusters. The aim of this paper is to design an automated tool for brain tumor detection using MRI scanned image data sets.

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