MRI BRAIN IMAGE SEGMENTATIN AND CLASSIFICATION BY MODIFIED FCM &SVM AKORITHM

Gopal B Deshmukh, Poonam Lambhate

Abstract: Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes.

Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.

DOI: https://doi.org/10.15623/ijret.2013.0212114