RT Journal Article
A1 Jan Odstrcilik
AD Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic
AD St. Anne's University Hospital – International Clinical Research Center (ICRC), Pekarska 53, 65691, Brno, Czech Republic
A1 Radim Kolar
AD Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic
AD St. Anne's University Hospital – International Clinical Research Center (ICRC), Pekarska 53, 65691, Brno, Czech Republic
A1 Attila Budai
AD Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany
A1 Joachim Hornegger
AD Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany
A1 Jiri Jan
AD Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic
A1 Jiri Gazarek
AD Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic
A1 Tomas Kubena
AD Ophthalmology Clinic M.D. Tomas Kubena, U zimniho stadionu 1759, 760 00, Zlin, Czech Republic
A1 Pavel Cernosek
AD Ophthalmology Clinic M.D. Tomas Kubena, U zimniho stadionu 1759, 760 00, Zlin, Czech Republic
A1 Ondrej Svoboda
AD Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic
A1 Elli Angelopoulou
AD Pattern Recognition Lab, University of Erlangen-Nuremberg, Martensstrasse 3, 91058, Erlangen, Germany

PB iet
T1 Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database
JN IET Image Processing
VO 7
IS 4
SP 373
OP 383
AB 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.
K1 retinal vessel segmentation
K1 retinal blood vessel tree
K1 high-resolution fundus image database
K1 pathological retinas
K1 computer-aided analysis
K1 eye diagnosis
K1 automatic assessment
K1 high-resolution colour fundus images
K1 improved matched filtering
K1 public screening
K1 vessel diameters
K1 diseases
K1 matched flltering
DO https://doi.org/10.1049/iet-ipr.2012.0455
UL https://digital-library.theiet.org/;jsessionid=y199vr9tuuvw.x-iet-live-01content/journals/10.1049/iet-ipr.2012.0455
LA English
SN 1751-9659
YR 2013
OL EN