Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation
This work addresses feature selection for medical image analysis, specifically retinal vessel segmentation, but is incremental as it compares existing heuristics.
The study compared six feature selection heuristics for retinal vessel segmentation using the DRIVE database, finding that the relief heuristic outperformed others in sensitivity, specificity, and accuracy.
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.