Abstract
It is proposed to present on an application of segmentation and classification of (MR) brain surgical images using fuzzy based control theory, because segmentation and classification of surgical images play a vital role both in diagnosing human diseases and analyzing the human anatomy. Both the identification and the analysis of a tumor in brain are complex processes and to overcome this complexity, image segmentation is preferred. The proposed Fuzzy Inference System with certain values of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) offers a promising part in identifying the tumor in brain. The result obtained assures that the proposed methodology has an efficient performance. In the Fuzzy Inference System methodology, fuzzy rules are coined by using membership function that helps in segmenting the image. The segmentation of (MR) brain images is done by using Fuzzy logic because it reduces the rate of misclassification. The content and the data of the image are changed with a minimized rate during segmentation.
Keywords: Magnetic resonance (MR) Brain image segmentation, Fuzzy inference system, Peak signal to noise ratio, Mean square error.
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