Detection of brain tumor is very common fatality in current scenario of health care society. Image segmentation is used to extract the abnormal tumor portion in brain. Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably, apparently unregulated by mechanisms that control cells. Segmentation of brain tissue in the magnetic resonance image (MRI) is very important for detecting and existence of outlines the brain tumor. In this research an algorithm for segmentation based on the symmetry character of brain image is presented. Our goal is to detect the position and edge of tumors automatically. Experiments were carried on real pictures, and the results show that the algorithm is flexible and convenient. This proposed method is more efficient and faster to identify the detecting the tumor region from T1, T2-weighted MRI brain images. The proposed Neural Network technique consists of some stages, namely, feature extraction, dimensionality reduction, detection, segmentation and classification. In this paper, the purposed method is more accurate and effective for the brain tumor detection and segmentation. Various techniques have been formulated for detection of tumor in brain. Our main concentration is on the techniques which use image segmentation to detect brain tumor. Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed by medical experts. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab Tool.

Brain Tumor, GA, And Image Segmentation.

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