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fMRI Segmentation Using Echo State Neural Network
D.Suganthi, Dr.S.Purushothaman
Pages - 1 - 9     |    Revised - 15-02-2008     |    Published - 30-02-2008
Volume - 2   Issue - 1    |    Publication Date - February 2008  Table of Contents
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KEYWORDS
Echo state neural network, Intelligent segmentation, functional magnetic resonance imaging, Back-propagation algorithm, Feature Extraction, Peak signal to noise ratio
ABSTRACT
This research work proposes a new intelligent segmentation technique for functional Magnetic Resonance Imaging (fMRI). It has been implemented using an Echostate Neural Network (ESN). Segmentation is an important process that helps in identifying objects of the image. Existing segmentation methods are not able to exactly segment the complicated profile of the fMRI accurately. Segmentation of every pixel in the fMRI correctly helps in proper location of tumor. The presence of noise and artifacts poses a challenging problem in proper segmentation. The proposed ESN is an estimation method with energy minimization. The estimation property helps in better segmentation of the complicated profile of the fMRI. The performance of the new segmentation method is found to be better with higher peak signal to noise ratio (PSNR) of 61 when compared to the PSNR of the existing back-propagation algorithm (BPA) segmentation method which is 57.
CITED BY (13)  
1 Satyawana, S. (2016). Novel Technique for Video Segmentation and Image Matching. Science, Engineering and Technology, 59.
2 Sampath, R., & Saradha, A. Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network.
3 Meftah, B., Lézoray, O., & Benyettou, A. (2015). Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation. Cognitive Computation, 1-9.
4 Yasmin, M., Sharif, M., Mohsin, S., & Azam, F. (2014). Pathological Brain Image Segmentation and Classification: A Survey. Current Medical Imaging Reviews, 10(3), 163-177.
5 Rajeswari, R., Irudhayaraj, A., & Purushothaman, S. error entropy minimization for brain image registration using hilbert-huang transform and echo state neural network.
6 Sangamnerkar, G. V., & Bhoyar, K. K. Color Image Segmentation in HSI Color Space Based on Color JND Histogram.
7 Pandian, A., & Sadiq, A. K. (2013). Authorship Attribution in Tamil Language Email for Forensic Analysis. International Review on Computers and Software (IRECOS), 8(12), 3002-3008.
8 Khan, W. (2013). Image Segmentation Techniques: A Survey. Journal of Image and Graphics, 1(4), 166-170.
9 Tungar, D. V., & Chaudhari, D. R. A Study of different techniques for Image Segmentation.Khan, Z. F., & Quadri, S. U. Automated Segmentation of Optical Nerves by Neural Network based Region Growing.
10 Souahlia, A., Belatreche, A., Benyettou, A., & Curran, K. An experimental evaluation of echo state network for colour image segmentation.
11 Vijayamadheswaran, R., Arthanari, M., & Sivakumar, M. (2011). performance comparison of neural networks for identification of diabetic retinopathy. International Journal of Computer Science and Information Security, 9(12), 29.
12 Mohanty, S., & Bebartta, H. N. D. (2010). A Novel Approach for Bilingual (English-Oriya) Script Identification and Recognition in a Printed Document. International Journal of Image Processing (IJIP), 4(2), 175.
13 Bhoyar, K., & Kakde, O. (2010). Color image segmentation based on JND color histogram. International Journal of Image Processing (IJIP), 3(6), 283.
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1 Rumelhart D.E., Hinton G.E., and William R.J; Learning internal representations by error propagation, in Rumelhart, D.E., and McClelland, J.L.; Parallel distribution processing: Explorations in the microstructure of cognition; 1986.
2 Lippmann.R.P,; An Introduction to computing with neural nets, IEEE Transactions on ASSP Mag.,.35,4-22, 1987.
3 Jaeger, H.; The echo state approach to analyzing and training recurrent neural networks; (Tech. Rep. No. 148). Bremen: German National Research Center for Information Technology, 2001.
4 Brian Parker, Three-Dimensional Medical Image Segmentation Using a Graph-Theoretic Energy-Minimisation Approach ,Biomedical & Multimedia Information Technology (BMIT) Group, Copyright © 2002,
5 Jaeger, H;. Short term memory in echo state networks; (Tech. Rep. No. 152) Bremen: German National Research Center for Information Technology. 2002.
6 Jaeger, H.; Tutorial on training recurrent neural networks, covering BPPT, RTRL,EKF and the “echo state network” approach (Tech. Rep. No. 159).; Bremen: German National Research Center for Information Technology, 2002.
7 Sinisa Pajevica and Peter J. Basser, Parametric and non-parametric statistical analysis of DTMRI data, Journal of Magnetic Resonance,1–14, 2003.
8 Thacker N. A., D. C. Williamson, M. Pokric; Voxel Based Analysis of Tissue Volume MRI Data;20th July 2003
9 Ola Friman and Carl-Fredrik Westin; Resampling fMRI time series; NeuroImage 25, 2005
10 Alan Wee-Chung Liew and Hong Yan; Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images; Current Medical Imaging Reviews, 2006.
11 D. Poot, J. Sijbers,3A. J. den Dekker, R. Bos;Estimation of the noise variance from the background histogram mode of an mr image, Proceedings of SPS-DARTS,2006
12 Hayit Greenspan, Amit Ruf, and Jacob Goldberger, Constrained Gaussian Mixture Model Framework for Automatic Segmentation of MR Brain Images, IEEE Transactions On Medical Imaging, 25 (9), SEPTEMBER 2006
13 Abdelouahab Moussaoui, Nabila Ferahta, and Victor Chen; A New Hybrid RMN Image Segmentation Algorith.; Transactions On Engineering, Computing And Technology, 12 ,2006
14 Ruth Heller, Damian Stanley, Daniel Yekutieli, Nava Rubin, and Yoav Benjamini; Clusterbased analysis of FMRI data; NeuroImage 599–608, 2006
15 Greg Heckenberg, Yongjian Xi, Ye Duan, and Jing Hua; Brain Structure Segmentation from MRI by Geometric Surface Flow; International Journal of Biomedical Imaging ,1–6, 2006
Mr. D.Suganthi
- India
suganthi_d2@yahoo.com
Mr. Dr.S.Purushothaman
- India