Abstract: In order to improve the effect of medical ultrasonic image in clinical diagnosis, it was necessary to optimize the detection and recognition of images, we proposed a multi-scale edge detection algorithm for medical ultrasonic images based on deep residual network. Firstly, the gray scale distribution matrix of medical ultrasonic image was constructed by automatically tagging the original medical ultrasonic image, and the multi-scale segmentation of medical ultrasonic image was completed by using the distribution matrix. Secondly, the contour model of multi-scale edge of medical ultrasonic image was constructed to extract the edge features of multi-scale image. Thirdly, the deep residual network structure was constructed, and the deep residual learning algorithm was used to fuse the underlying image information of ultrasonic image. Finally, multi-scale edge detection was performed on the fused edge image data. The experimental results show that the proposed algorithm has high accuracy of image segmentation, the accuracy of feature extraction is more than 80%, the detection effect of discontinuous area in the image boundary is good, the edge point checking is high, the detection time of the algorithm is short, and the convergence is strong.

Key words: deep residual network, medical ultrasonic image, multi-scale, edge detection