|
Abstract:
|
A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis
(SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance
Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the
inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is
divided into different clusters by a spectral distance based clustering. Then, Independent Component
Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM)
for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted,
T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the
performance of the new method. Comparative analysis with ICA based SVM and other conventional
classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction
of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto
Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced
lesions. Experimental results recommend the proposed method as a promising approach in clinical and
pathological studies of brain diseases |