Application Of Edge Detection Algorithm Based On Prior Knowledge In Medical Image

In recent years,with the rapid development of computer aided diagnosis system and telemedicine in medicine,digital image processing is very important.The edge of the image covers most of the image information and edge detection of medical image is the basis of subsequent image processing.Therefore,the research of medical image edge detection has important practical significance.The application and prospect of medical image,digital image processing and classical edge detection algorithm are described and analyzed in detail.At present,total variation model of edge detection and mathematical morphology algorithm are one of the mainstream algorithms of image edge detection,which have important research value in medical image processing.For medical images,edge detection of mixed noise is a challenging task,because the distribution of mixed noise usually has no parametric model.Based on the mixed gaussian and Cauchy noises in medical images,a MAP based variational edge detection model of Gaussian and Cauchy noises is proposed in this paper,and the ADMM method is used to solve the model.The robustness and effectiveness of the variational model for edge detection are verified by numerical simulation.Finally,in addition to visual analysis,the ROC and AUC objective indicators verify that the model is better than other classical edge detection algorithms.Due to the influence of single light source and detection means in the process of medical image imaging,the image noise distribution will be uneven and often mixed with a variety of different noises.Therefore,the edge detection algorithm applicable to visible images is not applicable to medical images.This paper proposes an improved morphological algorithm based on the characteristics of medical image,which contains the following three advantages.First,adaptive weight assignment.For multi-direction structure elements,the algorithm adaptively assigns the weight of each direction according to the distance of edge Mahalanobis gray scale.For multi-scale and multi-shape structural elements,the weight of each structural element is adaptively assigned according to the information entropy.Second,the improved morphological operator.Based on the disadvantages of the existing operators to detect jagged edges and have insignificant anti-noise effect,a new anti-noise morphological operator is proposed in this algorithm.Third,it is applied to edge detection of color medical image with mixed noise.In order to verify the robustness of the proposed algorithm,the proposed algorithm is applied to four kinds of color images with mixed noise for edge detection,and the detection results are good.Finally,visual analysis and objective evaluation indicators are used to verify that the proposed algorithm is better than other algorithms.Experimental results show that the image edge extracted by the algorithm in this paper is complete and clear,and has obvious advantages in the suppression and elimination of various mixed noises,which has a good application value in medical image research.