Abstract

Info

Title:: Retinal vessel diameter measurement based on depth learning and two-dimensional Gaussian fitting

Author(s):: LIU Haikun1; WANG Jian2; YANG Song1; WU Jun1; YIN Changshun1; ZHANG Kai1; ZHANG Zhen1; PEI Xinran1; WU Shuai1; 1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China; 2. Department of Electronics, Chinese People’s Liberation Army Air Force 93756, Tianjin 300131, China

Keywords:: ; Keywords: retinal vessel; fully convolutional network; diameter measurement; two-dimensional Gaussian fitting

Abstract:: Abstract: Diseases such as diabetes and hypertension can lead to changes in the shape of retinal blood vessels. Segmentation of fundus images is critical in the quantitative analysis of diseases, which is instructive in the clinical analysis and diagnosis of diseases. Herein a method based on depth learning and two-dimensional Gaussian fitting is proposed to automatically measure the diameters of retinal vessels. Firstly, the connected channel signatures and the network depth which is increased by depthwise separable convolutions are applied into fully convolutional network to segment the vessel network. Then, morphological refinement and least-squares fitting are used to find the centerline and direction of blood vessels in the segmented vessel network. Finally, based on gray value distribution characteristics of blood vessel cross-section, two-dimension Gaussian fitting is used to correct the centerlines and directions of retinal vessels for obtaining the accurate centerlines and directions of retinal vessels and then measuring the diameters of retinal vessels. Three image sets in the REVIEW database is tested. The standard deviation obtained with the proposed method is close to that of the manual measurement, which indicates that the proposed method can achieve a high accuracy in vessel diameter measurement. The validity of the proposed method is proved by the test on the REVIEW database.