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Fuzzy C-Means Clustering, Magnetic Resonance Imaging, Multisignal wavelet analysis, Multispectral analysis, Principal Component Analysis
In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.
Sindhumol S,Kannan Balakrishnan,Anil Kumar,"Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis", IJIGSP, vol.5, no.8, pp.29-36, 2013. DOI: 10.5815/ijigsp.2013.08.04
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