ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

Abstract

Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors. In this research, we propose a new multi-modal framework for vessel segmentation called ELEMENT (vEsseL sEgmentation using Machine lEarning and coNnecTivity). This framework consists of feature extraction and pixel-based classification using region growing and machine learning. The proposed features capture complementary evidence based on grey level and vessel connectivity properties. The latter information is seamlessly propagated through the pixels at the classification phase. ELEMENT reduces inconsistencies and speeds up the segmentation throughput. We analyze and compare the performance of the proposed approach against state-of-the-art vessel segmentation algorithms in three major groups of experiments, for each of the ocular modalities. Our method produced higher overall performance, with an overall accuracy of 97.40%, compared to 25 of the 26 state-of-the-art approaches, including six works based on deep learning, evaluated on the widely known DRIVE fundus image dataset. In the case of the STARE, CHASE-DB, VAMPIRE FA, IOSTAR SLO and RC-SLO datasets, the proposed framework outperformed all of the state-of-the-art methods with accuracies of 98.27%, 97.78%, 98.34%, 98.04% and 98.35%, respectively.

References in corpus (10)

• Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
• DUNet: A deformable network for retinal vessel segmentation
• Deep Vessel Segmentation By Learning Graphical Connectivity
• Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier
• Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
• A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography
• A Novel Deep Learning Pipeline for Retinal Vessel Detection in Fluorescein Angiography
• k-MS: A novel clustering algorithm based on morphological reconstruction
• Morphological classifiers
• Fractal triangular search: a metaheuristic for image content search

Cited by in corpus (7)

• Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network
• Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset
• Line Art Colorization of Fakemon using Generative Adversarial Neural Networks
• Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation
• X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing
• Universal Vessel Segmentation for Multi-Modality Retinal Images
• Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement