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

arXiv: https://arxiv.org/abs/2605.20458 · HTML: https://mlsanity.com/paper/2605.20458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 82 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 8 of 8)

- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)
- [Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier](https://mlsanity.com/paper/2112.12549/) (2021)
- [A Novel Deep Learning Pipeline for Retinal Vessel Detection in Fluorescein Angiography](https://mlsanity.com/paper/1907.02946/) (2019)
- [k-MS: A novel clustering algorithm based on morphological reconstruction](https://mlsanity.com/paper/2208.14390/) (2022)
- [Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images](https://mlsanity.com/paper/1603.04833/) (2016)
- [Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network](https://mlsanity.com/paper/1904.05644/) (2019)

## Built on by (most cited first, 3 of 3)

- [Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement](https://mlsanity.com/paper/2605.25307/) (2026)
- [Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation](https://mlsanity.com/paper/2605.21251/) (2026)
- [Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset](https://mlsanity.com/paper/2605.23777/) (2026)

## Abstract (arXiv)

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.