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

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. 1. Introduction Multispectral analysis of Magnetic Resonance Imaging (MRI) to access the relevant and complementary information from different sequences has been a widely discussed research topic for many years [1, 2]. MRI sequences like T1-weighted images (T1WI), T2-weighted images (T2WI), Proton Density Images (PDI), Fluid Attenuated Inversion Recovery (FLAIR), and so forth provide a huge repository of unique information on different tissues [2, 3]. For example, considerable contrast between Gray Matter (GM) and White Matter (WM) is available from T1WI. T2WI can give details of Cerebral Spinal Fluid (CSF) and abnormalities, whereas FLAIR images suppress CSF effects to give hyperintense lesions details. Simultaneous analysis of each sequence to collect the prominent pathological information is a tedious job for radiology experts. Computer-aided diagnosis using multispectral approach is helpful in this context to save time and to improve the accuracy and consistency of the clinical results [4]. But conventional algorithms used in normal data mining process are not efficient and robust to yield good results with expected clinical