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A novel approach for segmenting computer tomography lung images using Echo State Neural Networks
Z. Faizal Khan, S. Veeramalai, G. Nalini Priya, M. Ramesh Kumar, A. Naresh Kumar,
Published in Little Lion Scientific
2014
Volume: 68
   
Issue: 3
Pages: 504 - 513
Abstract
Segmentation is an important step for finding out the different portions of an image. Existing segmentation algorithms have involved many stages like elimination of blood vessels, tissues and finally showing the nodule in the segmented image. This paper proposes new segmentation technique using recurrent Echo State Neural Network (ESNN) method on computer tomography (CT) lung image. ESNN has been chosen in this work since it reduces the number of steps in segmentation to identify the presence of nodules in the CT lung image. The performance of ESNN segmentation has been shown to be the best when compared with other conventional segmentation algorithms like ‘Sobel’, ‘Prewitt’, ‘Robertz’, ‘Log’, ‘Zerocross’, ‘Canny’ and Contextual clustering. Matlab regionprops function has been used as one of the criteria to show the performance of segmentation algorithms. From this research work, it has been observed that the segmentation accuracy of the proposed algorithm has been achieved to 84.40%. © 2005 - 2014 JATIT & LLS. All rights reserved.
About the journal
JournalJournal of Theoretical and Applied Information Technology
PublisherLittle Lion Scientific
ISSN19928645