Performance Comparison of Publicly Available Retinal Blood Vessel Segmentation Methods

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Performance Comparison of Publicly Available Retinal Blood Vessel Segmentation Methods. / Vostatek, Pavel ; Claridge, Ela; Uusitalo, Hannu; Hauta-Kasari, Markku; Fält, Pauli; Lensu, Lasse.

In: Computerized Medical Imaging and Graphics, Vol. 55, 01.01.2017, p. 2-12.

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Vostatek, Pavel ; Claridge, Ela ; Uusitalo, Hannu ; Hauta-Kasari, Markku ; Fält, Pauli ; Lensu, Lasse. / Performance Comparison of Publicly Available Retinal Blood Vessel Segmentation Methods. In: Computerized Medical Imaging and Graphics. 2017 ; Vol. 55. pp. 2-12.

Bibtex

@article{bdf463e076ea48b5a66944fee6820fe8,
title = "Performance Comparison of Publicly Available Retinal Blood Vessel Segmentation Methods",
abstract = "Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels.Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods for each database by the linear regression model is tested. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors.The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.",
keywords = "Fundus, Retinal imaging, Vessel segmentation",
author = "Pavel Vostatek and Ela Claridge and Hannu Uusitalo and Markku Hauta-Kasari and Pauli F{\"a}lt and Lasse Lensu",
year = "2017",
month = jan,
day = "1",
doi = "10.1016/j.compmedimag.2016.07.005",
language = "English",
volume = "55",
pages = "2--12",
journal = "Computerized Medical Imaging and Graphics",
issn = "0895-6111",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Performance Comparison of Publicly Available Retinal Blood Vessel Segmentation Methods

AU - Vostatek, Pavel

AU - Claridge, Ela

AU - Uusitalo, Hannu

AU - Hauta-Kasari, Markku

AU - Fält, Pauli

AU - Lensu, Lasse

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels.Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods for each database by the linear regression model is tested. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors.The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

AB - Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels.Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods for each database by the linear regression model is tested. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors.The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

KW - Fundus

KW - Retinal imaging

KW - Vessel segmentation

UR - http://www.medicalimagingandgraphics.com/article/S0895-6111(16)30070-2/pdf

U2 - 10.1016/j.compmedimag.2016.07.005

DO - 10.1016/j.compmedimag.2016.07.005

M3 - Article

VL - 55

SP - 2

EP - 12

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

SN - 0895-6111

ER -