Brain MR image classification using multi-scale geometric analysis of ripplet

Progress in Electromagnetics Research

Volumn 137, Issue , 2013, Pages 1-17

  Das, Sudeb ^a   Chowdhury, Manish ^a   Kundu, Malay K ^a  

^a INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN MR IMAGES; CLASSIFICATION ACCURACY; CROSS VALIDATION; DIGITAL IMAGE; FEATURE VECTORS; GENERALIZATION CAPABILITY; HUMAN BRAIN; MR IMAGES; MULTI-SCALE GEOMETRIC ANALYSIS; MULTISCALE GEOMETRIC ANALYSIS (MGA); PERFORMANCE COMPARISON; RIPPLET TRANSFORMS; SALIENT FEATURES; STATE-OF-THE-ART TECHNIQUES;

CLASSIFICATION (OF INFORMATION); EXPERIMENTS; FACE RECOGNITION; IMAGE RETRIEVAL; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MATHEMATICAL TRANSFORMATIONS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

IMAGE CLASSIFICATION;

EID: 84873725415     PISSN: 10704698     EISSN: 15598985     Source Type: Journal    
DOI: 10.2528/PIER13010105     Document Type: Article

References (23)

1. Westbrook, C., Handbook of MRI Technique, John Wiley & Sons, 2008.
2. Scapaticci, R., L. Di Donato, I. Catapano, and L. Crocco, "A feasibility study on microwave imaging for brain stroke monitoring, " Progress In Electromagnetics Research B, Vol. 40, 305-324, 2012.
3. Prasad, P. V., Magnetic Resonance Imaging: Methods and Biologic Applications (Methods in Molecular Medicine), Humana Press, 2005.
4. Asimakis, N. P., I. S. Karanasiou, P. K. Gkonis, and N. K. Uzunoglu, "Theoretical analysis of a passive acoustic brain monitoring system, " Progress In Electromagnetics Research B, Vol. 23, 165-180, 2010.
5. Mohsin, S. A., N. M. Sheikh, and U. Saeed, "MRI induced heating of deep brain stimulation leads: Effect of the air-tissue interface, " Progress In Electromagnetics Research, Vol. 83, 81-91, 2008.
6. Maji, P., M. K. Kundu, and B. Chanda, "Second order fuzzy measure and weighted co-occurrence matrix for segmentation of brain MR images, " Fundamenta Informaticae, Vol. 88, Nos. 1-2, 161-176, 2008.
7. Golestanirad, L., A. P. Izquierdo, S. J. Graham, J. R. Mosig, and C. Pollo, "Effect of realistic modeling of deep brain stimulation on the prediction of volume of activated tissue, " Progress In Electromagnetics Research, Vol. 126, 1-16, 2012.
8. Mohsin, S. A., "Concentration of the specific absorption rate around deep brain stimulation electrodes during MRI, " Progress In Electromagnetics Research, Vol. 121, 469-484, 2011.
9. Rombouts, S. A., F. Barkhof, and P. Scheltens, Clinical Applications of Functional Brain MRI, Oxford University Press, 2007.
10. Oikonomou, A., I. S. Karanasiou, and N. K. Uzunoglu, "Phased array near field radiometry for brain intracranial applications, " Progress In Electromagnetics Research, Vol. 109, 345-360, 2010.
11. Maji, P., B. Chanda, M. K. Kundu, and S. Dasgupta, "Deformation correction in brian MRI using mutual information and genetic algorithm, " Proc. Int. Conf. Computing: Theory and Applications, 372-376, 2007.
12. Zhang, Y., L. Wu, and S. Wang, "Magnetic resonance brain image classification by an improved artificial bee colony algorithm, " Progress In Electromagnetics Research, Vol. 116, 65-79, 2011.
13. Chaplot, S., L. M. Patnaik, and N. R. Jagannathan, "Classifica-tion magnetic resonance brain images using wavelets as input to support vector machine and neural network, " Biomedical Signal Processing and Control, Vol. 1, No. 1, 86-92, 2006.
14. Maitra, M. and A. Chatterjee, "A Slantlet transform based intel-ligent system for magnetic resonance brain image classification, " Biomedical Signal Processing and Control, Vol. 1, No. 4, 299-306, 2006.
15. El-Dahshan, E.-S. A., T. Hosny, and A.-B. M. Salem, "Hybrid intelligent techniques for MRI brain images classification, " Digital Signal Processing, Vol. 20, No. 2, 433-441, 2010.
16. Zhang, Y., S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO, " Progress In Electromagnetics Research, Vol. 109, 325-343, 2010.
17. Zhang, Y., Z. Dong, L. Wu, and S. Wang, "A hybrid method for MRI brain image classification, " Expert Systems with Applications, Vol. 38, No. 8, 10049-10053, 2011.
18. Zhang, Y. and L. Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine, " Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.
19. Xu, J., L. Yang, and D. Wu, "Ripplet: A new transform for image processing, " Journal of Visual Communication and Image Representation, Vol. 21, No. 7, 627-639, 2010.
20. Candes, E. J. and D. L. Donoho, "Continuous curvelet transform: I. Resolution of the wavefront set, " Applied and Computational Harmonic Analysis, Vol. 19, No. 2, 162-197, 2005.
21. Das, S., M. Chowdhury, and M. K. Kundu, "Medical image fusion based on ripplet transform type-I, " Progress In Electromagnetics Research B, Vol. 30, 355-370, 2011.
22. Jolliffe, I. T., Principal Component Analysis, Springer, 2002.
23. Suykens, J. A. K. and J. Vandewalle, "Least squares support vector machine classifiers, " Neural Processing Letters, Vol. 9, No. 3, 293-300, 1999.