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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 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 languageEnglish (US)
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs
StatePublished - Dec 12 2012
Externally publishedYes
Event2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain
Duration: Sep 23 2012Sep 26 2012

Publication series

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

Other

Other2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
CountrySpain
CitySantander
Period9/23/129/26/12

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

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  • 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 [6349730] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349730