A novel edge detection in medical images by fusing of multi-model from different spatial structure clues
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: Beijing Municiple Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing, 100124, P.R. China | College of Computer Science, Beijing University of Technology, Beijing, 100124, P.R. China
Note: [] Corresponding author: Jia Xibin, College of Computer Science, #100 Ping Le Yuan, Chaoyang District, Beijing 100124, P.R. China. Tel.: +86 10 67392370; E-mail: jiaxibin@bjut.edu.cn.
Abstract: Edge detection has been widely used in medical image processing, automatic diagnosis, et al. A novel edge detection algorithm, based on the fusion model, is proposed by combination with the two proposed models as follows: the matrix of most probable distribution of edge point and the matrix of the difference weight of each point. The most probable distribution of edge point can be obtained by analyzing the variance among 4-connected neighborhood points around each pixel under estimation in the image to label the all candidate edge points in the image. The difference weight of each point can be gotten by analyzing the brightness difference between the neighborhood point and the under-estimating pixel to represent the probability of being edge. The two matrices gotten from the different descriptions of spatial structure are fused together and derive from the final edge image with thresholding method on the fusion matrix. The experiments are performed based on the public diabetic retinopathy database DRIVE. According to the edge images obtained, the proposed method is subjectively analyzed to be complete and close to the Ground Truth image with very low noise in comparison with the Sobel, Canny and LOG edge detectors. The F1 measure, ROC measure and PFOM measure are separately adopted to make quantitative evaluation of the proposed edge detection algorithm. Experimental results show that the proposed method is able to improve the effect of edge detection on medical images.
