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Brain Image Segmentation based on U-Net Architecture with Adaptive Histogram Equalization

In this paper, a brain tumor segmentation method has been proposed. Image segmentation is required for the detection of brain tumors. For this purpose, brain images are divided into two distinct areas. This is one of the main but also the most complex element of the tumor identification process. A brain tumor is a result of mass of tissue that grows, it is the most important cause of the increased mortality rate among children as well as adults. This paper proposes a brain tumor segmentation method by using a combination of Adaptive Histogram Equalization and U-Net architecture. The performance of U-Net architecture and U-Net Architecture with Adaptive Histogram Equalization Technique are contrast by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), Quality and Complexity parameters. Overlap-tile strategy is used to smooth the segmentation of large images. Many MR images were collected, and the observations were carried out for statistical analysis using the Adaptive Histogram Equalization and U-Net architecture.