Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps

Bekir Dizdaro; Esra Ataer-Cansizoglu; Jayashree Kalpathy-Cramer; Katie Keck; Michael F. Chiang; Deniz Erdogmus

doi:10.1109/MLSP.2012.6349730

Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps

Bekir Dizdaro, Esra Ataer-Cansizoglu, Jayashree Kalpathy-Cramer, Katie Keck, Michael F. Chiang, Deniz Erdogmus

Ophthalmology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

Abstract

In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.

Original language	English (US)
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs	https://doi.org/10.1109/MLSP.2012.6349730
State	Published - 2012
Event	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain Duration: Sep 23 2012 → Sep 26 2012

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Other

Other	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Country/Territory	Spain
City	Santander
Period	9/23/12 → 9/26/12

Keywords

Fundus image
level sets
phase map for edge detection
principal curves as ridges
retinal vasculature analysis
vessel segmentation

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing

Access to Document

10.1109/MLSP.2012.6349730

Cite this

Dizdaro, B., Ataer-Cansizoglu, E., Kalpathy-Cramer, J., Keck, K., Chiang, M. F., & Erdogmus, D. (2012). Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. In 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012 Article 6349730 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349730

Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. / Dizdaro, Bekir; Ataer-Cansizoglu, Esra; Kalpathy-Cramer, Jayashree et al.
2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012. 2012. 6349730 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dizdaro, B, Ataer-Cansizoglu, E, Kalpathy-Cramer, J, Keck, K, Chiang, MF & Erdogmus, D 2012, Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. in 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012., 6349730, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012, Santander, Spain, 9/23/12. https://doi.org/10.1109/MLSP.2012.6349730

Dizdaro B, Ataer-Cansizoglu E, Kalpathy-Cramer J, Keck K, Chiang MF, Erdogmus D. Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. In 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012. 2012. 6349730. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2012.6349730

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abstract = "In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.",

keywords = "Fundus image, level sets, phase map for edge detection, principal curves as ridges, retinal vasculature analysis, vessel segmentation",

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AB - In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.

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Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps

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