Open AccessAutomatic Segmentation of Microaneurysm in Retinal Fundus Images
Author(s) -
Guodong Zhang,
HasinduMaduranga Suraweera,
AdevianFairuz Pratama,
Dazhe Zhao,
Zhaoxuan Gong,
Wei Guo
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072023
Subject(s) - artificial intelligence , computer science , segmentation , pattern recognition (psychology) , context (archaeology) , computer vision , convolutional neural network , diabetic retinopathy , feature extraction , medicine , diabetes mellitus , paleontology , biology , endocrinology
Diabetes interferes with the body’s ability to use and store sugar (glucose), which it can cause damage throughout the body. Generally, Microaneurysm (MA) is the first clinically observable lesions in the Diabetic Retinopathy (DR). Since interference of biological objects in eye fundus images are similar to MA, locate Microaneurysm lesions precisely is a challenging task for researches. This research employs U-Net Convolutional Neural Network to model saliency of objects in images, global and local context are both taken into account to provide a better initialization for the process. Feature extraction techniques are applied first to assign a local saliency value to each pixel by considering its local context from fundus images such as ORB, SURF, and MSER. The extracted feature vectors are applied for training the network. The sum of the weighted salient object regions produce the final saliency map, then implements U-Net to segment MA lesions.Our experiment has carried out using the publicly available Indian Diabetic Retinopathy Image Dataset (IDRiD), which has used in "Diabetic Retinopathy: Segmentation and Grading Challenge" workshop and our proposed method has given an outstanding accuracy of 98.78 %.