Abstract

Info

Title:: Medical image segmentation based on visual saliency of feature fusion

Author(s):: WU Di1; HU Sheng2; LIU Weifeng3; HU Lingzhi1; HU Junhua1; 1. School of Basic Medical Science, Shaanxi University of Chinese Medicine, Xianyang 712046, China; 2. School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China; 3. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Keywords:: ; medical image segmentation; visual saliency; feature attribute; feature fusion

Abstract:: Abstract: The accuracy of medical image segmentation results is of great value for the doctor to diagnose the disease and develop appropriate treatment strategies. In view of the problem that the existing medical image segmentation methods have not considered the human visual saliency which leads to a lower segmentation accuracy, we propose a method for medical image segmentation based on the visual saliency of feature fusion. Firstly, based on frequency tuning, a saliency map is generated for the medical images to be segmented in order to obtain the salient regions and highlight the edge of the medical images. Then the color features and texture features are extracted separately to form the input vectors of back propagation neural network. Finally, the back propagation neural network model is used to achieve medical image segmentation. The proposed method is verified by experiments, and the results show that the proposed visual saliency of feature fusion algorithm for medical images segmentation could achieve a high efficiency and an ideal accuracy. The proposed method provides a new way for medical image segmentation.