A comparative study on feature selection for retinal vessel segmentation using FABC

Carmen Alina Lupascu; Domenico Tegolo; Emanuele Trucco

doi:10.1007/978-3-642-03767-2_80

A comparative study on feature selection for retinal vessel segmentation using FABC

Carmen Alina Lupascu, Domenico Tegolo, Emanuele Trucco

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns, Proceedings
Subtitle of host publication	13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings
Editors	Xiaoyi Jiang, Nicolai Petkov
Place of Publication	Berlin
Publisher	Springer
Pages	655-662
Number of pages	8
ISBN (Print)	9783642037665
DOIs	https://doi.org/10.1007/978-3-642-03767-2_80
Publication status	Published - 2009
Event	13th International Conference on Computer Analysis of Images and Patterns - Munster, Germany Duration: 2 Sept 2009 → 4 Sept 2009 http://cvpr.uni-muenster.de/CAIP2009/index.html

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	5702

Conference

Conference	13th International Conference on Computer Analysis of Images and Patterns
Abbreviated title	CAIP 2009
Country/Territory	Germany
City	Munster
Period	2/09/09 → 4/09/09
Internet address	http://cvpr.uni-muenster.de/CAIP2009/index.html

Keywords

Retinal images
vessel segmentation
AdaBoost classifier
feature selection
BLOOD-VESSELS
MATCHED-FILTERS
IMAGES
CLASSIFICATION

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10.1007/978-3-642-03767-2_80

Cite this

Lupascu, C. A., Tegolo, D., & Trucco, E. (2009). A comparative study on feature selection for retinal vessel segmentation using FABC. In X. Jiang, & N. Petkov (Eds.), Computer Analysis of Images and Patterns, Proceedings: 13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings (pp. 655-662). (Lecture notes in computer science; Vol. 5702). Springer . https://doi.org/10.1007/978-3-642-03767-2_80

Lupascu, Carmen Alina ; Tegolo, Domenico ; Trucco, Emanuele. / A comparative study on feature selection for retinal vessel segmentation using FABC. Computer Analysis of Images and Patterns, Proceedings: 13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings. editor / Xiaoyi Jiang ; Nicolai Petkov. Berlin : Springer , 2009. pp. 655-662 (Lecture notes in computer science).

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title = "A comparative study on feature selection for retinal vessel segmentation using FABC",

abstract = "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.",

keywords = "Retinal images, vessel segmentation, AdaBoost classifier, feature selection, BLOOD-VESSELS, MATCHED-FILTERS, IMAGES, CLASSIFICATION",

author = "Lupascu, {Carmen Alina} and Domenico Tegolo and Emanuele Trucco",

year = "2009",

doi = "10.1007/978-3-642-03767-2_80",

language = "English",

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pages = "655--662",

editor = "Jiang, {Xiaoyi } and Petkov, {Nicolai }",

booktitle = "Computer Analysis of Images and Patterns, Proceedings",

note = "13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009 ; Conference date: 02-09-2009 Through 04-09-2009",

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Lupascu, CA, Tegolo, D & Trucco, E 2009, A comparative study on feature selection for retinal vessel segmentation using FABC. in X Jiang & N Petkov (eds), Computer Analysis of Images and Patterns, Proceedings: 13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings. Lecture notes in computer science, vol. 5702, Springer , Berlin, pp. 655-662, 13th International Conference on Computer Analysis of Images and Patterns, Munster, Germany, 2/09/09. https://doi.org/10.1007/978-3-642-03767-2_80

A comparative study on feature selection for retinal vessel segmentation using FABC. / Lupascu, Carmen Alina; Tegolo, Domenico; Trucco, Emanuele.
Computer Analysis of Images and Patterns, Proceedings: 13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings. ed. / Xiaoyi Jiang; Nicolai Petkov. Berlin: Springer , 2009. p. 655-662 (Lecture notes in computer science; Vol. 5702).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A comparative study on feature selection for retinal vessel segmentation using FABC

AU - Lupascu, Carmen Alina

AU - Tegolo, Domenico

AU - Trucco, Emanuele

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Retinal images

KW - vessel segmentation

KW - AdaBoost classifier

KW - feature selection

KW - BLOOD-VESSELS

KW - MATCHED-FILTERS

KW - IMAGES

KW - CLASSIFICATION

U2 - 10.1007/978-3-642-03767-2_80

DO - 10.1007/978-3-642-03767-2_80

M3 - Conference contribution

SN - 9783642037665

T3 - Lecture notes in computer science

SP - 655

EP - 662

BT - Computer Analysis of Images and Patterns, Proceedings

A2 - Jiang, Xiaoyi

A2 - Petkov, Nicolai

PB - Springer

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T2 - 13th International Conference on Computer Analysis of Images and Patterns

Y2 - 2 September 2009 through 4 September 2009

ER -

Lupascu CA, Tegolo D, Trucco E. A comparative study on feature selection for retinal vessel segmentation using FABC. In Jiang X, Petkov N, editors, Computer Analysis of Images and Patterns, Proceedings: 13th International Conference, CAIP 2009, Münster, Germany, September 2-4, 2009. Proceedings. Berlin: Springer . 2009. p. 655-662. (Lecture notes in computer science). doi: 10.1007/978-3-642-03767-2_80

A comparative study on feature selection for retinal vessel segmentation using FABC

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Keywords

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