Image segmentation is a task that is fundamental many image processing and computer vision applications. Due to the existence of noise, low contrast, and intensity in homogeneity, it really is still a difficult issue in majority of applications. One of the steps that are first way of understanding images is to segment them in order to find down different objects inside them. However, in real images such as MRI graphics, noise is corrupting the image information or image usually consists of textured sections. The images produced by MRI scans are frequently grey images with strength in the product range scale that is gray. The MRI image associated with the brain comprises of the cortex that lines the surface that is outside of brain additionally the gray nuclei deep inside of the mind including the thalami and basal ganglia. As Cancer may be the leading cause of death for all as the explanation for the condition remains unknown, very early detection and diagnosis is one of the keys to cancer control, and it will increase the success of treatment, save lives and reduce expenses. Health imaging is very often used tools which can be diagnostic detect and classify defects. To eliminate the dependence of the operator and increase the precision of diagnosis system aided diagnosis computer are a valuable and ensures that are advantageous the detection of cancer tumors and classification. Segmentation techniques based on gray level techniques such as for instance threshold and methods based on region are the easiest and find application that is restricted. However, their performance can be improved by incorporating them with the ways of hybrid clustering. practices based on textural characteristics atlas that is using look-up table can have very good results on the segmentation of medical pictures , however, they require expertise within the construction of the atlas Limiting the technical atlas based is that , in some circumstances , it becomes difficult to choose correctly and label information has difficulty in segmenting complex structure with variable form, size and properties such circumstances it is best to use unsupervised methods such as fuzzy algorithms. In this work we proposed a novel fuzzy based MRI Image Segmentation algorithm, Fuzzy Segmentation involves the task of dividing data points into homogeneous classes or clusters making sure that things within the same class are as similar as possible and items in numerous classes are as dissimilar as you can.