Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Abstract

Methods for image registration, segmentation, and visualization of magnetic resonance imaging (MRI) data are used widely to help medical doctors in supporting diagnostics. The large amount and complexity of MRI data require looking for new methods that allow for efficient processing of this data. Here, we propose using the adaptive independent subspace analysis (AISA) method to discover meaningful electroencephalogram activity in the MRI scan data. The results of AISA (image subspaces) are analyzed using image texture analysis methods to calculate first order, gray-level co-occurrence matrix, gray-level size-zone matrix, gray-level run-length matrix, and neighboring gray-tone difference matrix features. The obtained feature space is mapped to the 2D space using the t-distributed stochastic neighbor embedding method. The classification results achieved using the k-nearest neighbor classifier with 10-fold cross-validation have achieved 94.7% of accuracy (and f-score of 0.9356) from the real autism spectrum disorder dataset.