Independent Component Analysis for Magnetic Resonance Image Analysis

Abstract

Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected.

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

“…ICA is a potential and promising approach in structural and functional MRI analysis [21]. The main concept of this technique lies in unmixing a set of independent sources according to their statistical independency from a linearly mixed input signal [10].…”

“…It is considered as an over-complete problem since < ; that is, there are fewer images than the sources to be unmixed. According to linear system of equations, there exist many solutions to solve (1) and there is no way to select best ICs to perform classification [21]. If the number of signal sources is greater than the number of ICs, more than one signal characteristics are accommodated in the same IC.…”

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.…”

Unsupervised Classification for Volume-Based Magnetic Resonance Brain Images

“…INTRODUCTION Current computer aided design techniques developed for MR image classification suffers from several issues such as (1) how to find an appropriate set of training samples, (2) how to select an effective classifier to be used for classification, and (3) how to process a complete set of MR image slices as an image cube instead of processing individual MR slices. Among these three issues it is the third issue which is most intriguing and challenging due to the fact that MR image slices vary with different locations, each MR image slice requires its specific training samples and one set of training samples obtained for one slice is not necessarily applicable to another slice.…”