6533b833fe1ef96bd129c449

RESEARCH PRODUCT

A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC

Domenico TegoloEmanuele TruccoCarmen Alina Lupascu

subject

PixelSettore INF/01 - InformaticaComputer sciencebusiness.industryFeature vectorRetinal images vessel segmentation AdaBoost classifier feature selection.ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionFeature selectionFeature (computer vision)SegmentationComputer visionArtificial intelligenceHeuristicsbusinessFeature detection (computer vision)

description

This paper presents a comparative study on five feature selection heuristics applied to a retinal image database called DRIVE. Features are chosen from a feature vector (encoding local information, but as well information from structures and shapes available in the image) constructed for each pixel in the field of view (FOV) of the image. After selecting the most discriminatory features, an AdaBoost classifier is applied for training. The results of classifications are used to compare the effectiveness of the five feature selection methods.

yearjournalcountryeditionlanguage
2009-01-01
10.1007/978-3-642-03767-2_80http://hdl.handle.net/10447/40109