Segmentation of Breast Mass and Diagnosis of Benign and Malignant Breast Tumors Based on Edge Constraint in Pulse Coupled Neural Network

Abstract

In order to segment breast tumor accurately, an improved Unit-Linking Pulse-Coupled Neural Networks based mammography image segmentation method is proposed. Firstly, the link input and coupled parameter in the original model are improved according to the relationship between this neuron and its neighbors. Then, the improved model is used to segment the breast tumor image to obtain multiple output images. Finally, the gradient algorithm is used to calculate the edges of the original image and each output image respectively, and the minimum mean square error (MMSE) of the two edge images is calculated to find the best output image. The final experimental results indicate that the improved method can accurately segment breast tumor images in different environments. In addition, based on the segmentation results, we use the SVM method to diagnose the type of tumor, and its classification accuracy is much higher than the existing deep classification algorithm.