Journal article


Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database


Publication Details
Author(s): Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J, Gazarek J, Kubena T, Cernosek P, Svoboda O, Angelopoulou E
Publisher: Institution of Engineering and Technology (IET)
Publication year: 2013
Volume: 7
Journal issue: 4
Pages range: 373-383
ISSN: 1751-9659

Abstract

Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database.



How to cite
APA: Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J.,... Angelopoulou, E. (2013). Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Processing, 7(4), 373-383. https://dx.doi.org/10.1049/iet-ipr.2012.0455

MLA: Odstrcilik, Jan, et al. "Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database." IET Image Processing 7.4 (2013): 373-383.

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