Medicine Image Enhancement And Edge Detect Based On Granular Computing

Granular computing theory is a tool to deal with imprecise, uncertain knowledge. medical image contains complex and vast amounts of information. because of medical image's feature.the use of granular computing theory in medical image, the effect of the image enhancement and edge detection is so good.In this work,a new medical image enhancement's method based on granular computing and unsharp mask. Chest CT images were again used in this work. the main idea is take the medical image was treated as a knowledge system. Using the indiscernible relation of granular computing the medical image pixels were classified to target and background areas according to their medical and background properties. The target area and background area were denoised generating denoised subgraphs. The contrast of such subgraphs was enhanced. Details of the graphs were finally enhanced by unsharp mask image processing technique. This work used both the variance and the proposed method to treat chest CT images and to evaluate the correctness of the resultant enhanced image which validates the current method over traditional one.The main purpose of medical image edge detection is to strengthen the details of the edges. In this work, the theory of granular computing was used to divide the image under study into subgraphs of target and background areas. These two subgraphs were denoised. The information of edges in the denoised subgraphs was extracted according to edge properties. Finally, the output image was given. Chest CT images were again used in this work. In comparison with traditional methods, current one gives better results which verify the effectiveness of the method. In this experiment take the original Chest CT image and enhanced Chest CT image were detect edge. based on the edge detection's three principles for evaluation the results. the result of the enhanced Chest CT image better than the original Chest CT image.Both methods are effective in treating medical images to achieve a good result which can help doctors to make precise diagnosis with high efficiency.