The Study Of Medical Image Enhancement And Edge Detection Algorithm Based On Matlab

Objective:In order to improve medical image quality, get more valuable information from the result and provide the foundations for the clinical diagnosis and analysis, this task aimed at the application of image enhancement and edge detection algorithm. Medical image processing enhancing the image knowability, thereby, it is more satisfying to observe and process image. Medical image enhancement mostly selectively extrude the interested characters of image, give prominence to profile of targets, enhance the details of image, advance the arrangements, filtrate all kinds of yawps and so on. The result of edge detection can offer the important characteristic and definitely distinguish the focus of medical image, for making preparations for the image processing, such as further identifying target and comminuting image.Methods:1. Utilized the toolbox function of Matlab to pretreat the images, changing the original images into gray scale images. Respectively processed several medical images which collected by histogram equalization and histogram regulation. Afterward, analyzed and compared the ending images after processing.2. Chose templates 5×5 and 3×3. Utilized the toolbox function of Matlab to respectively process several collected medical images by Wiener filtering to self-adaption Wiener filtering and by median filtering to analyze the processing effects of algorithms to medical images.3. With Matlab program, several collected medical images were processed edge detection by Roberts operator, Prewitt operator, Sobel operator, Laplacian and Canny operator. Analyzed and compared with their detection results.Result:1. Utilized the algorithms of histogram equalization and histogram regulation, changing the dense gray scale original image into sparse one. After processing, the images vision effects improved. Histogram equalization enhanced the details not distinct, while histogram regulation could keep the unclear details in focus.2. The images processed by 5×5 template Wiener filtering after adding gauss noise had faint of edges and decreased the definition of parenchyma in images. While, the images processed by 3×3 template Wiener filtering had better effects of images vision. The images processed by 3×3 median filtering could basicly get rid of the noise, and the images changed clearer.3. Roberts operator orientated the edges not correctly, while, Sobel operator and Prewitt operator could detect the edges but discontiguous and comparatively faint. Sobel operator could pick up more details information compared with Prewitt operator. Laplacian operator brought the artifical information when detected the edge. Canny operator could really detect the faint edge. Conlusion:1. Processing medical images by histogram can improve the image quality effectively and keep the image in focus. On the sake of processing part-detail medical image, histogram regulation is better than histogram equalization.2. Wiener filtering and median filtering can eliminate the noise of medical image effectively, so that it can help for analysis and diagnosis of medical image.3. Utilizing all kinds of differential coefficient operators to pick up edge for medical image, discovered that Canny operator could make the profile more flat, clear, integrated and controlling noise was better than other differential coefficient operators when to pick up the edge.Through the above tasks,the image processing technics discussed by this thesis are fit for clinic diagnosis.Contraposing not engineering course clinical doctor,these methods are practical, convinence,concise and efficient,diagnosing many deseases more betimes and nicety.