Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Abstract

Purpose: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. Materials and Methods:Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICAþSVM method to analyze three sets of MR images, T1-weighted, T2-weighted, and proton density/fluid-attenuated inversion recovery images.Results: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICAþSVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICAþSVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra-and inter-operator coefficient of variations. Conclusion:The experiments conducted provide evidence that the ICAþSVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI.

Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses

“…In this case, we have to solve an over-determined problem, as opposed to the under-determined problem that must be solved with fMRI data, where there is at least one IC which must accommodate multiple tissue clusters due to the lack of data dimensionality. To resolve this dilemma where there are insufficient ICs to deal with the large number of brain tissue (signal sources) while preserving the ability of ICA to enhance contrast brain images, the support vector machine has been proposed for incorporation in conjunction with the ICA to improve its separation abilities (Chai et al, 2010).…”

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

“…ICA is a good preprocessing step in multispectral classification. It separates the mixed input signals into statistically independent components (ICs), from which an improved brain tissue classification can be performed in MRI analysis [14]. However, it ignores small details while processing massive amount of information.…”

“…Tanimoto Index, the most commonly used measurement in medical imaging [14], can be measured by comparing the reproduced tissues with ground truth using the formula…”

“…Recent brain MRI studies using ICA [13,14] and its extensions like kernel ICA [13], overcomplete ICA [15], and so forth, have demonstrated ICA as a good feature extraction method for normal and abnormal tissue analysis. As a global transform, ICA gives more priority to global features, thereby losing important information on local brain matters in classification [16].…”

A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

“…Hyperspectral imaging has recently emerged as an advanced technique in remote sensing to deal with many issues that cannot be resolved by multispectral imaging, specifically, subpixel target detection and mixed pixel classification [9]. Its applications to MRI classification have been also explored in [10][11][12][13][14][15]. However, it seems that using the concept of hyperspectral imaging techniques for WMH detection in brain MRI has not been investigated.…”