Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation

TL;DR

This paper compares six feature selection heuristics using an ant colony system to identify the most relevant features for retinal vessel segmentation, aiming to improve classification accuracy and efficiency.

Contribution

It provides a comparative analysis of feature selection heuristics applied to retinal image data, highlighting the effectiveness of the relief heuristic in this context.

Findings

Relief heuristic outperformed other heuristics in feature selection.

Selected features improved segmentation accuracy metrics.

Ant colony system effectively evaluated heuristic performance.

Abstract

Features selection is an essential step for successful data classification, since it reduces the data dimensionality by removing redundant features. Consequently, that minimizes the classification complexity and time in addition to maximizing its accuracy. In this article, a comparative study considering six features selection heuristics is conducted in order to select the best relevant features subset. The tested features vector consists of fourteen features that are computed for each pixel in the field of view of retinal images in the DRIVE database. The comparison is assessed in terms of sensitivity, specificity, and accuracy measurements of the recommended features subset resulted by each heuristic when applied with the ant colony system. Experimental results indicated that the features subset recommended by the relief heuristic outperformed the subsets recommended by the other experienced heuristics.