Retinal vasculature segmentation using principal spanning forests

Erhan Bas, Esra Ataer-Cansizoglu, Deniz Erdogmus, Jayashree Kalpathy-Cramer

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

6 Scopus citations

Abstract

We propose an automated morphology reconstruction method for curvilinear network analysis. The proposed approach first projects samples to the ridge of the intensity image of the curvilinear system. Then, a manifold deviation measure is utilized to approximate the ridge with piecewise linear segments between the projected samples. A nonparametric system workflow based on the kernel interpolation and density estimation is provided for the derivations without any user defined meta-parameter, i.e. hard threshold for segmentation. Lastly, a rigorous sampling strategy using the manifold deviation measure that can be used for robust sparse tree reconstruction is provided. The proposed approaches have been tested on a small set of representative retinal scans. Preliminary qualitative results indicate the effectiveness of the method.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages1792-1795
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • Principal graphs
  • resampling on manifolds

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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