Optic Disc Detection Based on Fully Convolutional Network and Weighted Matrix Recovery Model

作     者：Wang, Siqi Yu, Xiaosheng Chi, Jianning Lv, Pengfei Wang, Junxiang Wu, Chengdong 

作者机构：Faculty of Robot Science and Engineering Northeastern University NO. 195 Chuangxin Road Liaoning Shenyang110170 China 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Semantics 

摘      要：Accurate detection of the optic disc contour is one of the important steps in diagnosing eye diseases. Considering that the optic disc is usually a saliency region in fundus images, we propose a weakly-supervised optic disc detection method based on the fully convolution neural network (FCN) combined with the weighted low-rank matrix recovery model (WLRR). Firstly, we extract the low-level features of the fundus image and cluster the pixels using the Simple Linear Iterative Clustering (SLIC) algorithm to generate the feature matrix. Secondly, the top-down semantic prior information provided by FCN and bottom-up background prior information of the optic disc region are used to jointly construct the prior information weighting matrix, which more accurately guides the decomposition of the feature matrix into a sparse matrix representing the optic disc and a low-rank matrix representing the background. Experimental results show that our method can segment the optic disc region accurately. © 2023, The Authors. All rights reserved.