Weak Edge Detection Algorithm for Medical Images Based on Full Convolution Neural Network - LI Xiao-feng; LI Dong; WANG Yan-wei (Journal of Harbin University of Science and Technology)
Aiming at the existing image weak edge detection algorithm，it is easy to ignore the selection of threshold in edge calculation，and there is no clustering analysis of the data，which leads to the problem that the detection effect is not good. A weak edge detection algorithm for complex medical images based on full convolution neural network is proposed. Firstly，Mean-shift is used to filter the complex medical image，and the gray pixel of the filtered image is enhanced． Secondly，the adaptive dynamic threshold method is used to determine the edge points and internal candidate points of the image，and the edge calculation results are obtained． Lastly，the full convolution neural network model is established，and the model is trained，and the calculated image edges are inputted into the model. The objective function of clustering is constructed and solved by quantum genetic clustering method，and the clustering of input data is completed. Based on the clustering results，the edge information