|
| fMRI Segmentation Using Echo State Neural Network
|
|
Full
text: |
 PDF(244.1KB) |
|
|
Source |
International Journal of Image Processing (IJIP) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(1.27MB) |
|
Volume: 2 Issue: 1 |
| |
Pages: 1-34 |
|
Publication
Date: February 2008 |
|
ISSN
(Online): 1985-2304 |
|
|
|
|
|
Pages |
1 - 9 |
|
Author(s) |
|
|
|
Published
Date |
30-02-2008 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Echo state neural network, Intelligent segmentation, functional magnetic resonance imaging, Back-propagation algorithm, Feature Extraction, Peak signal to noise ratio |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. OpenJ-Gate |
| 3. Docstoc |
| 4. Scribd |
| 5. PDFCAST |
| 6. Google Scholar |
| 7. CiteSeerX |
| 8. WorldCat |
| 9. ScientificCommons |
| 10. refSeek |
| 11. Academic Index |
| 12. Bielefeld Academic Search Engine (BASE) |
| 13. ResearchGATE |
| 14. ResearchGATE |
| 15. Microsoft Academic Search |
| 16. iSEEK |
| 17. Socol@r |
| |
|
| |
|
|
| 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. |
| |
|
| |
|
| |
| 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
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| D.Suganthi : Colleagues
|
|
| Dr.S.Purushothaman : Colleagues
|
|
