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Automated Segmentation of Optical Nerves by Neural Network based Region Growing

Z. Faizal Khan, Syed Usama Quadri Published in Automated Systems

Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal
10.5120/cae-1543

Faizal Z Khan and Syed Usama Quadri. Article: Automated Segmentation of Optical Nerves by Neural Network based Region Growing. Communications on Applied Electronics 1(5):9-13, April 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Z. Faizal Khan and Syed Usama Quadri},
	title = {Article: Automated Segmentation of Optical Nerves by Neural Network based Region Growing},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {1},
	number = {5},
	pages = {9-13},
	month = {April},
	note = {Published by Foundation of Computer Science, New York, 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.

Reference

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Keywords

Contextual clustering, Segmentation Algorithm, Retinal image.