Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

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.

[1]  James F. Greenleaf,et al.  Use of gray value distribution of run lengths for texture analysis , 1990, Pattern Recognit. Lett..

[2]  Hamid A. Jalab,et al.  Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge , 2016, Symmetry.

[3]  Myung Jun Lee,et al.  Spatial correlation and segregation of multimodal MRI abnormalities in multiple system atrophy , 2018, Journal of Neurology.

[4]  A. Galaburda,et al.  Regional volumetric abnormalities in pediatric autism revealed by structural magnetic resonance imaging , 2018, International Journal of Developmental Neuroscience.

[5]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[6]  Belur V. Dasarathy,et al.  Image characterizations based on joint gray level-run length distributions , 1991, Pattern Recognit. Lett..

[7]  Kim-Anh Lê Cao,et al.  Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets , 2012, BMC Bioinformatics.

[8]  Steven C. R. Williams,et al.  Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach , 2010, The Journal of Neuroscience.

[9]  A. Estes,et al.  Early Identification of Autism Spectrum Disorder: Recommendations for Practice and Research , 2015, Pediatrics.

[10]  Konrad P. Körding,et al.  Extracting Slow Subspaces from Natural Videos Leads to Complex Cells , 2001, ICANN.

[11]  M. Kendall.  Statistical Methods for Research Workers , 1937, Nature.

[12]  S. Rose,et al.  A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective , 2018, International Journal of Developmental Neuroscience.

[13]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[14]  E Le Rumeur,et al.  MRI texture analysis on texture test objects, normal brain and intracranial tumors. , 2003, Magnetic resonance imaging.

[15]  R. Blanco,et al.  Interventional and intraoperative MRI at low field scanner--a review. , 2005, European journal of radiology.

[16]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[17]  Dimitri Van De Ville,et al.  Data-driven tensor independent component analysis for model-based connectivity neurofeedback , 2019, NeuroImage.

[18]  Hao Chen,et al.  Automatic cerebral microbleeds detection from MR images via Independent Subspace Analysis based hierarchical features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  D. Okada,et al.  Digital Image Processing for Medical Applications , 2009 .

[20]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[21]  Eero P. Simoncelli,et al.  Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.

[22]  Bernard Fertil,et al.  Texture indexes and gray level size zone matrix. Application to cell nuclei classification , 2009 .

[23]  Jean-François Cardoso,et al.  Dependence, Correlation and Gaussianity in Independent Component Analysis , 2003, J. Mach. Learn. Res..

[24]  Michael A. Casey,et al.  Separation of Mixed Audio Sources By Independent Subspace Analysis , 2000, ICMC.

[25]  Kalyani R. Joshi,et al.  A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer's disease , 2018, Biomed. Signal Process. Control..

[26]  Wagner Coelho A. Pereira,et al.  Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound , 2012, IEEE Transactions on Medical Imaging.

[27]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[28]  Aapo Hyvärinen,et al.  Topographic Independent Component Analysis , 2001, Neural Computation.

[29]  Dinggang Shen,et al.  Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI , 2018, Front. Hum. Neurosci..

[30]  Farid Meziane,et al.  Automated screening of MRI brain scanning using grey level statistics , 2016, Comput. Electr. Eng..

[31]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[32]  Nooshin Nabizadeh,et al.  Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features , 2015, Comput. Electr. Eng..