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A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps

Published: 23 June 2016 Publication History

Abstract

In this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database.

References

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M. Martinez-Perez, A. Hughes, S. Thom, A. Bharath, K. Parker, Segmentation of blood vessels from red-free and fluorescein retinal images, Medical Image Analysis 11 (2007) 47--61.
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M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, S. Barman, Blood vessel segmentation methodologies in retinal images -- a survey, computer Methods and Programs in Biomedicine 108 (2012) 407--433.
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M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, SPIE Medical Imaging 5370 (2004) 648--656.
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V. B. J. Soares, J. G. J. Leandro, R. M. J. Cesar, F. H. Jelinek, M. J. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification, IEEE Transactions on Medical Imaging 25 (2006) 1214--1222.
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A. M. Mendonca, A. Campilho, Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Trans Med Imaging 25 (2006) 1200--1213.
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C. A. Lupascu, D. Tegolo, E. Trucco, FABC: Retinal Vessel Segmentation Using AdaBoost, IEEE Trans on Information Technology in Biomedicine 14 (2010) 1267--1274.
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C. A. Lupascu, D. Tegolo, Stable automatic unsupervised segmentation of retinal vessels using Self-Organizing Maps and a modified Fuzzy C-Means clustering, proc. of WILF 2011, Lecture Notes in Computer Science 6857 (2011) 244--252.
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T. Kohonen, Self-organization and associative memory, Springer-Verlag, Berlin, 1989.
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  • (2020)SHADOW PROCESSING TECHNOLOGY OF AGRICULTURAL PLANT VIDEO IMAGE BASED ON PROBABLE LEARNING PIXEL CLASSIFICATIONINMATEH Agricultural Engineering10.35633/inmateh-60-23(201-210)Online publication date: 30-Apr-2020

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CompSysTech '16: Proceedings of the 17th International Conference on Computer Systems and Technologies 2016
June 2016
466 pages
ISBN:9781450341820
DOI:10.1145/2983468
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • UORB: University of Ruse, Bulgaria
  • TECHUVB: Technical University of Varna, Bulgaria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2016

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Author Tags

  1. CLAHE
  2. Fuzzy C-Means
  3. Retinal Vessels
  4. STAPLE
  5. Self-Organizing Map
  6. majority voting

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  • Refereed limited

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CompSysTech '16

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CompSysTech '16 Paper Acceptance Rate 55 of 117 submissions, 47%;
Overall Acceptance Rate 241 of 492 submissions, 49%

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View all
  • (2020)SHADOW PROCESSING TECHNOLOGY OF AGRICULTURAL PLANT VIDEO IMAGE BASED ON PROBABLE LEARNING PIXEL CLASSIFICATIONINMATEH Agricultural Engineering10.35633/inmateh-60-23(201-210)Online publication date: 30-Apr-2020

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