Brain Tumor Edge Detection based on GWT and DWT Fusioning

Brain tumor Edge detection plays a vital role in medical image processing. Edge of the image is one of the most significant features which is mainly used for image analyzing process. An edge is in general a border which separates the adjacent zones of image having distinct brightness. The development of an edge detector is often based on a specific characteristic of the image. An important property of the edge detection method is its ability to extract the accurate edge line with good orientation, and much literature on edge detection has been published in the past three decades. It is encountered in the areas of feature selection and feature extraction in Computer Vision. An edge detector accepts a digital image as input and produces an edge map as output. The edge maps of some detectors include explicit information about the position and strength of the edges and their orientation. An efficient algorithm for extracting the edge features of images using Gabor Wavelet and fusion technique is proposed in this work. Gabor wavelets can effectively abstract local and discrimination features. In textural analysis and image segmentation, GW features have achieved outstanding results, while in machine vision, they found to be effective in object detection, recognition and tracking. The most useful application of the Gabor Wavelets is for edge detection. Gabor wavelet along with DWT and fusion based approach is highly effective at detecting both the brain tumor location and orientation of edges. The results proved that the performance of proposed method is superior to conventional Gabor Wavelet and other edge detection algorithms. The performance of the proposed method is proved with the help of Entropy, PSNR & spectral analysis is also computed for proposed method edge detected output image, gabor wavelet edge detected output image, original input MRI brain image.