CFP last date
01 April 2024
Reseach Article

Automated Segmentation of Optical Nerves by Neural Network based Region Growing

by Z. Faizal Khan, Syed Usama Quadri
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2015
Authors: Z. Faizal Khan, Syed Usama Quadri
10.5120/cae-1543

Z. Faizal Khan, Syed Usama Quadri . Automated Segmentation of Optical Nerves by Neural Network based Region Growing. Communications on Applied Electronics. 1, 5 ( April 2015), 9-13. DOI=10.5120/cae-1543

@article{ 10.5120/cae-1543,
author = { Z. Faizal Khan, Syed Usama Quadri },
title = { Automated Segmentation of Optical Nerves by Neural Network based Region Growing },
journal = { Communications on Applied Electronics },
issue_date = { April 2015 },
volume = { 1 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 2394-4714 },
pages = { 9-13 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume1/number5/328-1543/ },
doi = { 10.5120/cae-1543 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T18:38:04.023477+05:30
%A Z. Faizal Khan
%A Syed Usama Quadri
%T Automated Segmentation of Optical Nerves by Neural Network based Region Growing
%J Communications on Applied Electronics
%@ 2394-4714
%V 1
%N 5
%P 9-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computer Aided Diagnosis (CAD) of retinal image has been a revolutionary step in the early diagnosis of diseases present in the eye. Developing an efficient and robust algorithm for optical nerve segmentation has been a demanding area of growing research of interest during the last two decades. The initial step in computer aided diagnosis of retinal image is generally to segment the nerves present in it and then to analyze each area separately in order to find the presence of pathologies present in it. This research reports on segmentation of the nerves by segmenting the retinal images using Echo State Neural Networks along with the combination of region growing algorithm. Region growing has been combined with ESNN in this work since it reduces the number of steps in segmentation for the process of identifying a tissue in the CT retinal image. The performance of this proposed segmentation is proved to be better when it is compared with other existing conventional segmentation algorithms. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy.

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, Massimo Torsello, Ilaria Zecca, Roberto Bellotti, Sabina Tangaro, Piero Calvini, Niccolò Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, and Piernicola Oliva, "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. Dr. Z. Faizal Khan, Dr. G. Nalini Priya, Dr A. Kannan, 'A novel Approach for Segmenting Computer Tomography Lung Images Using Echo State Neural Networks', Article in press, Journal of Theoretical and Applied Information technology.
  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 2002;19:105–12.
  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 2005;24:584–92.
  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 2004;21:84–90.
  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 1996;80:940–4.
  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. 1997. p. 524–5.
  9. Osareh A, Mirmehdi M, Thomas B, Markham R. Automated identification of diabetic retinal exudates in digital colour images. Br J Ophthalmol 2003;87:1220–3.
  10. Mitra SK, Te-Won Lee, Goldbaum M. Bayesian network based sequential inference for diagnosis of diseases from retinal images. Pattern Recogn Lett 2005;26:459–70.
  11. Purushothaman S and Suganthi D, 2008, fMRI segmentation using echo state neural network, International Journal of Image Processing, Vol. 2, Issue 1, pp. 1-9.
Index Terms

Computer Science
Information Sciences

Keywords

Contextual clustering Segmentation Algorithm Retinal image.