Table of Contents
ISRN Biomedical Imaging
Volume 2013 (2013), Article ID 473437, 10 pages
http://dx.doi.org/10.1155/2013/473437
Research Article

Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

1Artificial Intelligence Lab, Department of Computer Applications, Cochin University of Science and Technology, Kochi 682022, India
2Institute of Radiology and Imaging Sciences, Indira Gandhi Co-Operative Hospital, Kochi, Kerala 682020, India

Received 4 February 2013; Accepted 26 March 2013

Academic Editors: W. Hall, I. Karanasiou, and G. Waiter

Copyright © 2013 Sindhumol S. et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multispectral analysis is a potential approach in simultaneous analysis of brain MRI sequences. However, conventional classification methods often fail to yield consistent accuracy in tissue classification and abnormality extraction. Feature extraction methods like Independent Component Analysis (ICA) have been effectively used in recent studies to improve the results. However, these methods were inefficient in identifying less frequently occurred features like small lesions. A new method, Multisignal Wavelet Independent Component Analysis (MW-ICA), is proposed in this work to resolve this issue. First, we applied a multisignal wavelet analysis on input multispectral data. Then, reconstructed signals from detail coefficients were used in conjunction with original input signals to do ICA. Finally, Fuzzy C-Means (FCM) clustering was performed on generated results for visual and quantitative analysis. Reproducibility and accuracy of the classification results from proposed method were evaluated by synthetic and clinical abnormal data. To ensure the positive effect of the new method in classification, we carried out a detailed comparative analysis of reproduced tissues with those from conventional ICA. Reproduced small abnormalities were observed to give good accuracy/Tanimoto Index values, 98.69%/0.89, in clinical analysis. Experimental results recommend MW-ICA as a promising method for improved brain tissue classification.