Research and Implementation of Medical Image Edge Detection Algorithm

Abstract

Medical imaging has become an important reference for the diagnosis of various diseases, and the role of medical imaging will become more and more important in the future. The interpretation of medical images is of paramount importance. With the continuous development of medical imaging technology, image interpretation has become more and more important. At present, it is directly inferred by doctors that in order to solve problems more effectively and deal with fuzzy data, it is necessary to research and implement an algorithm-based medical image edge detection assistant system. The current mainstream algorithms for edge detection include: Roberts, Sobel, Prewitt, etc. Most of these algorithms construct operators for small neighborhood pixels of the original image. The problem is that the algorithm is relatively sensitive to noise in the image and does not automatically select the appropriate threshold, resulting in a result that is not as expected. This is a disadvantage of current algorithms. The thesis elaborates on the theory and algorithm of image edge detection. At the same time, from the perspective of the original edge detection algorithm, the Canny algorithm is mainly studied, and the optimized MTM algorithm and Ostu algorithm are combined to study and optimize in the filtering denoising research. Finally, the algorithm is implemented in C++ language, which realizes the automatic extraction of the edge function of medical images under noise conditions. The improved algorithm performs an edge replacement effect change compared to the conventional algorithm.