CFP last date
15 January 2025
Call for Paper
February Edition
IJAIS solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 15 January 2025

Submit your paper
Know more
Reseach Article

Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering

by Z. Faizal Khan, Syed Usama Quadri
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 6
Year of Publication: 2015
Authors: Z. Faizal Khan, Syed Usama Quadri
10.5120/ijais2015451423

Z. Faizal Khan, Syed Usama Quadri . Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering. International Journal of Applied Information Systems. 9, 6 ( September 2015), 7-10. DOI=10.5120/ijais2015451423

@article{ 10.5120/ijais2015451423,
author = { Z. Faizal Khan, Syed Usama Quadri },
title = { Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering },
journal = { International Journal of Applied Information Systems },
issue_date = { September 2015 },
volume = { 9 },
number = { 6 },
month = { September },
year = { 2015 },
issn = { 2249-0868 },
pages = { 7-10 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number6/810-2015451423/ },
doi = { 10.5120/ijais2015451423 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:00:21.348266+05:30
%A Z. Faizal Khan
%A Syed Usama Quadri
%T Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 6
%P 7-10
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computer Aided Detection of medical image has been an improved step in the early diagnosis of diseases present in the body. Developing an efficient algorithm for medical image segmentation has been a demanding area of growing research of interest during the last decades. The initial step in computer aided diagnosis of retinal medical image is generally to segment the nerves present in it. The second step is to analyze each area separately to find the presence of pathologies in it. This paper reports on segmenting of the nerves by separating the retinal images using the combination of Improved Fuzzy-C-Means Clustering along with the Enhanced multidimensional multiscale parser (EMMP) algorithm. The performance of this proposed approach is proved to be better for a threshold value of 120. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 97.4 % in segmenting Retinal nerves.

References
  1. B. S. Morse, Lecture 18: Segmentation (Region Based), 1998-2000.
  2. Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo, Ivan De Mitri, Marco Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, “Automatic retinal Segmentation in CT Images with Accurate Handling of the Hilar Region”, Journal of digital imaging, Vol 24, No 1, pp 11-27, 2011.
  3. J. Quintanilla-Dominguez, B. Ojeda-Magaña, M. G. Cortina-Januchs, R. Ruelas, A. Vega-Corona, and D. Andina, "Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications," Sharif University of Technology Scientia Iranica, vol. 18, pp. 580–589, 8 February 2011 2011.
  4. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S. Automated detection of diabetic retinopathy on digital fundus image. J Diabet Med; 19:105–12, 2002.
  5. Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MS, Abramoff MD, Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imag; 24:584–92, 2005.
  6. Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med; 21:84–90, 2004
  7. Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol; 80:940–4, 1996
  8. Zheng Liu, Opas C, Krishnan SM. Automatic image analysis of fundus photograph. In: Proceedings of the International Conference on Engineering in Medicine and Biology, vol. 2, p. 524–5, 1997.
  9. Osareh A, Mirmehdi M, Thomas B, Markham R. Automated identification of diabetic retinal exudates in digital colour images. Br J Ophthalmol; 87: 1220–3, 2003.
  10. Mitra SK, Te-Won Lee, Goldbaum M. Bayesian network based sequential inference for diagnosis of diseases from retinal images. Pattern Recogn Letters, vol.26, pp- 459–470, 2005.
  11. Xizhao Wang, Yadong Wang, Lijuan Wang, “Improving fuzzy c-means clustering based on feature-weight learning”, pattern recognition letters, April 2004.
  12. Mahesh Yambal1, Hitesh Gupta,” Image Segmentation using Fuzzy C Means Clustering: A survey,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 7, July 2013.
  13. Yong yang,” image segmentation by fuzzy c-means clustering algorithm with a novel penalty term," computing and informatics, vol. 26, 17–31, 2007.
  14. Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M. (2011). A new supervised method for blood vessel segmentation in retinal images by using graylevel and moment invariants-based features. IEEE Transactions on Medical Imaging, 30 (1), 146-158.
  15. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transactions on Biomedical Engineering, 59 (9), 2538-2548.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy-C-Means Clustering Enhanced multidimensional multiscale parser (EMMP) algorithm Segmentation Retinal image.