Title :
Wavelet based independent component analysis for multispectral brain tissue classification
Author :
Sindhumol, S. ; Kumar, Ajit ; Balakrishnan, K.
Author_Institution :
Dept. of Comput. Applic., Cochin Univ. of Sci. & Technol., Kochi, India
Abstract :
Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions.
Keywords :
biological tissues; biomedical MRI; brain; diseases; feature extraction; image classification; image segmentation; independent component analysis; medical image processing; sensitivity analysis; source separation; wavelet transforms; MRI; Tanimoto index-sensitivity values; abnormality detection; classification; clinical applications; decorrelated detail coefficients; k-means clustering; local feature extraction; magnetic resonance images; multisignal wavelet analysis; multispectral analysis; multispectral brain tissue classification; real data; segmentation; small white matter lesions; source signal separation; synthetic data; wavelet based independent component analysis; Algorithm design and analysis; Brain; Feature extraction; Independent component analysis; Magnetic resonance imaging; Transforms; Wavelet analysis; Independent Component Analysis; Magnetic Resonance Imaging; Multispectral Analysis; Wavelet Transforms;
Conference_Titel :
Communications and Signal Processing (ICCSP), 2013 International Conference on
Conference_Location :
Melmaruvathur
Print_ISBN :
978-1-4673-4865-2
DOI :
10.1109/iccsp.2013.6577086