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 by the linear regression model is tested for each database. 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.
authors: Machine Vision and Pattern Recognition Laboratory, LUT School of Engineering Science, Lappeenranta University of Technology, PO Box 20, FI-53851 Lappeenranta, Finland.
authors: Pavel
authors: P
authors: Vostatek
authors: School of Computer Science, University of Birmingham, United Kingdom.
authors: Ela
authors: E
authors: Claridge
authors: Department of Ophthalmology/SILK, University of Tampere and Tays Eye Center, Tampere University Hospital, Finland.
authors: Hannu
authors: H
authors: Uusitalo
authors: School of Computing, University of Eastern Finland, Finland.
authors: Markku
authors: M
authors: Hauta-Kasari
authors: School of Computing, University of Eastern Finland, Finland.
authors: Pauli
authors: P
authors: Fält
authors: Machine Vision and Pattern Recognition Laboratory, LUT School of Engineering Science, Lappeenranta University of Technology, PO Box 20, FI-53851 Lappeenranta, Finland. Electronic address: lasse.lensu@lut.fi.
authors: Lasse
authors: L
authors: Lensu
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D012372:ROC Curve; D012171:Retinal Vessels / Q000000981:diagnostic imaging*
keywords: Fundus; Retinal imaging; Vessel segmentation