Robust Vessel Segmentation in Fundus Images
One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necess...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2013/154860 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849309108321124352 |
|---|---|
| author | A. Budai R. Bock A. Maier J. Hornegger G. Michelson |
| author_facet | A. Budai R. Bock A. Maier J. Hornegger G. Michelson |
| author_sort | A. Budai |
| collection | DOAJ |
| description | One of the most common modalities to examine the human eye is the
eye-fundus photograph. The evaluation of fundus photographs is carried
out by medical experts during time-consuming visual inspection. Our
aim is to accelerate this process using computer aided diagnosis. As a
first step, it is necessary to segment structures in the images for tissue
differentiation. As the eye is the only organ, where the vasculature can be
imaged in an in vivo and noninterventional way without using expensive
scanners, the vessel tree is one of the most interesting and important
structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of
high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average
accuracy above 94% and low computational needs. This outperforms state-of-the-art methods. |
| format | Article |
| id | doaj-art-0cbfb2f4cdf44b11b3af65d6e39d69df |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-0cbfb2f4cdf44b11b3af65d6e39d69df2025-08-20T03:54:15ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962013-01-01201310.1155/2013/154860154860Robust Vessel Segmentation in Fundus ImagesA. Budai0R. Bock1A. Maier2J. Hornegger3G. Michelson4Pattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, GermanyPattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, GermanyPattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, GermanyPattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, GermanyErlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, GermanyOne of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.http://dx.doi.org/10.1155/2013/154860 |
| spellingShingle | A. Budai R. Bock A. Maier J. Hornegger G. Michelson Robust Vessel Segmentation in Fundus Images International Journal of Biomedical Imaging |
| title | Robust Vessel Segmentation in Fundus Images |
| title_full | Robust Vessel Segmentation in Fundus Images |
| title_fullStr | Robust Vessel Segmentation in Fundus Images |
| title_full_unstemmed | Robust Vessel Segmentation in Fundus Images |
| title_short | Robust Vessel Segmentation in Fundus Images |
| title_sort | robust vessel segmentation in fundus images |
| url | http://dx.doi.org/10.1155/2013/154860 |
| work_keys_str_mv | AT abudai robustvesselsegmentationinfundusimages AT rbock robustvesselsegmentationinfundusimages AT amaier robustvesselsegmentationinfundusimages AT jhornegger robustvesselsegmentationinfundusimages AT gmichelson robustvesselsegmentationinfundusimages |