X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing

E O Rodrigues; L O Rodrigues; J J Lima; D Casanova; F Favarim; E R Dosciatti; V Pegorini; L S N Oliveira

cs.CVMay 19, 2026

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing

Authors: E O Rodrigues, L O Rodrigues, J J Lima, D Casanova, F Favarim, E R Dosciatti, V Pegorini, L S N Oliveira, +1 more

Organizations: Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTEPR), Pato Branco, Parana, Brazil · Graduate Program of Applied Sciences to Health Products, Universid ade Federal Fluminense(UFF), Niteroi, Rio de Janeiro, Brazil · Primary Health Care, Pato Branco Prefecture, Parana, Brazil · Innovation Office, Mass General Brigham Hospital, Cambridge, Massachusetts, United States of America

Abstract

This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.

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May 19, 2026 · cs.CVJ/K move · Enter open · S save

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

Erick O. Rodrigues, Aura Conci, Panos Liatsis

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.

Retinal Vessel Segmentation Fundus Images Unified Multimodal Models Machine Learning

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Abstract

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