%0 Electronic Article %A Jan Odstrcilik %+ Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic %+ St. Anne's University Hospital – International Clinical Research Center (ICRC), Pekarska 53, 65691, Brno, Czech Republic %A Radim Kolar %+ Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic %+ St. Anne's University Hospital – International Clinical Research Center (ICRC), Pekarska 53, 65691, Brno, Czech Republic %A Attila Budai %+ Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany %A Joachim Hornegger %+ Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany %A Jiri Jan %+ Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic %A Jiri Gazarek %+ Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic %A Tomas Kubena %+ Ophthalmology Clinic M.D. Tomas Kubena, U zimniho stadionu 1759, 760 00, Zlin, Czech Republic %A Pavel Cernosek %+ Ophthalmology Clinic M.D. Tomas Kubena, U zimniho stadionu 1759, 760 00, Zlin, Czech Republic %A Ondrej Svoboda %+ Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic %A Elli Angelopoulou %+ Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany %K retinal vessel segmentation %K retinal blood vessel tree %K high-resolution fundus image database %K pathological retinas %K computer-aided analysis %K eye diagnosis %K automatic assessment %K high-resolution colour fundus images %K improved matched filtering %K public screening %K vessel diameters %K diseases %K matched flltering %X 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. %@ 1751-9659 %T Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database %B IET Image Processing %D June 2013 %V 7 %N 4 %P 373-383 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=65angsr7r0kd4.x-iet-live-01content/journals/10.1049/iet-ipr.2012.0455 %G EN