Literature review of fMRI image processing techniques

Functional Magnetic Resonance imaging is an aid in identifying the brain activated regions by certain stimuli and tasks. It can be used to identify the psychological or other disease states resulting from various neurological impairments and also to make implication on brain connectivity. Several techniques are used for the functional analysis of fMRI neuroimaging data. Here our endeavor is to make a review of various computational methodologies used by fMRI data analysis, which includes various preprocessing methods used for removing noise and enhancing quality in fMRI as well as various statistical methods of fMRI image analysis.

[1]  D. Suganthi fMRI SEGMENTATION USING ECHO STATE NEURAL NETWORK , 2008 .

[2]  Chandrasekharan Kesavadas,et al.  fMRI paradigm designing and post-processing tools , 2014, Indian Journal of Radiology and Imaging.

[3]  G. Glover Overview of functional magnetic resonance imaging. , 2011, Neurosurgery clinics of North America.

[4]  Andrea Brovelli,et al.  Multivoxel Pattern Analysis for fMRI Data: A Review , 2012, Comput. Math. Methods Medicine.

[5]  Fuqian Shi,et al.  Shape Preserving Fitting Model for Affective Curves Extraction: An Affective Computing Method on fMRI Dataset , 2013 .

[6]  Stephen M. Smith,et al.  Pre-Processing of BOLD FMRI Data , 2006 .

[7]  M. Lindquist The Statistical Analysis of fMRI Data. , 2008, 0906.3662.

[8]  Jeroen van der Grond,et al.  Imaging the default mode network in aging and dementia. , 2012, Biochimica et biophysica acta.

[9]  Tinu Varghese,et al.  A review of neuroimaging biomarkers of Alzheimer's disease. , 2013, Neurology Asia.

[10]  Kaiming Li,et al.  Review of methods for functional brain connectivity detection using fMRI , 2009, Comput. Medical Imaging Graph..

[11]  Andrea Stocco,et al.  Coordinate-Based Meta-Analysis of fMRI Studies with R , 2014, R J..

[12]  Shengyong Chen,et al.  Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress , 2012, Comput. Math. Methods Medicine.

[13]  Martha Skup,et al.  Longitudinal fMRI analysis: A review of methods. , 2010, Statistics and its interface.

[14]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[15]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[16]  Ajay Somkuwar,et al.  Noise Reduction Techniques in Medical Imaging Data-A Review , 2013 .

[17]  Tutut Herawan,et al.  Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.

[18]  María Martín,et al.  Currently Available Neuroimaging Approaches in Alzheimer Disease (AD) Early Diagnosis , 2011 .

[19]  N. Hantke Predicting Cognitive Decline in Older Adults Through Multi-Voxel Pattern Analysis , 2014 .

[20]  J. Lagopoulos,et al.  Making sense of neuroimaging in psychiatry , 2007, Acta psychiatrica Scandinavica.

[21]  Jagdeep Kaur,et al.  fMRI Image Analysis using Pixel Neighborhood Segmentation Techniques , 2014 .

[22]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[23]  R. Sandak,et al.  Review on Analysis and Quantification of Specific Learning Disability (SLD) with fMRI using Image Processing Techniques , 2011 .

[24]  Gabriela Coelho de Pinho Queirós Computational Methods for fMRI image Processing and Analysis , 2013 .

[25]  R Baumgartner,et al.  Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. , 2000, Magnetic resonance imaging.

[26]  Yongwook Bryce Kim,et al.  Comparison of data-driven analysis methods for identification of functional connectivity in fMRI , 2008 .