Software multiplataforma para a segmenta\c{c}\~ao de vasos sangu\'ineos em imagens da retina

TL;DR

This paper introduces the first open-source, cross-platform software for manual and automated blood vessel segmentation in retinal images, aiming to improve machine learning models through user-annotated data.

Contribution

It presents an innovative integrated software combining manual segmentation, image filtering, and machine learning retraining for retinal vessel analysis.

Findings

Software is open-source and cross-platform.

Enhances vessel visualization with established filters.

Facilitates retraining of machine learning algorithms.

Abstract

In this work, we utilize image segmentation to visually identify blood vessels in retinal examination images. This process is typically carried out manually. However, we can employ heuristic methods and machine learning to automate or at least expedite the process. In this context, we propose a cross-platform, open-source, and responsive software that allows users to manually segment a retinal image. The purpose is to use the user-segmented image to retrain machine learning algorithms, thereby enhancing future automated segmentation results. Moreover, the software also incorporates and applies certain image filters established in the literature to improve vessel visualization. We propose the first solution of this kind in the literature. This is the inaugural integrated software that embodies the aforementioned attributes: open-source, responsive, and cross-platform. It offers a comprehensive solution encompassing manual vessel segmentation, as well as the automated execution of classification algorithms to refine predictive models.