Retinal vasculature segmentation using principal spanning forests

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

doi:10.1109/ISBI.2012.6235930

Retinal vasculature segmentation using principal spanning forests

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

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

5 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 language	English (US)
Title of host publication	2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2012 - Proceedings
Pages	1792-1795
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2012.6235930
State	Published - Aug 15 2012
Externally published	Yes
Event	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: May 2 2012 → May 5 2012

Publication series

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

Other

Other	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/Territory	Spain
City	Barcelona
Period	5/2/12 → 5/5/12

Keywords

Principal graphs
resampling on manifolds

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2012.6235930

Cite this

Bas, E., Ataer-Cansizoglu, E., Erdogmus, D., & Kalpathy-Cramer, J. (2012). Retinal vasculature segmentation using principal spanning forests. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings (pp. 1792-1795). [6235930] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2012.6235930

Retinal vasculature segmentation using principal spanning forests. / Bas, Erhan; Ataer-Cansizoglu, Esra; Erdogmus, Deniz et al.

2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings. 2012. p. 1792-1795 6235930 (Proceedings - International Symposium on Biomedical Imaging).

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

Bas, E, Ataer-Cansizoglu, E, Erdogmus, D & Kalpathy-Cramer, J 2012, Retinal vasculature segmentation using principal spanning forests. in 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings., 6235930, Proceedings - International Symposium on Biomedical Imaging, pp. 1792-1795, 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, Barcelona, Spain, 5/2/12. https://doi.org/10.1109/ISBI.2012.6235930

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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.",

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AB - 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.

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Retinal vasculature segmentation using principal spanning forests

Abstract

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Keywords

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