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

Domenico Tegolo; Emanuele Trucco

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

Domenico Tegolo, Emanuele Trucco

Matematica e Informatica

Risultato della ricerca: Other › peer review

19 Citazioni (Scopus)

Abstract

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

Lingua originale	English
Pagine	655-662
Numero di pagine	8
Stato di pubblicazione	Published - 2009

All Science Journal Classification (ASJC) codes

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

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

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