Dissimilarity-Based Detection of Schizophrenia

We propose to approach the detection of patients affected by schizophrenia by means of dissimilarity-based classification techniques applied to brain magnetic resonance images. Instead of working with features directly, pairwise distances between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments were carried out on a set of 64 patients and60 controls and several pairwise dissimilarity measurements have been analyzed. We demonstrate that good results are possible and especially significant improvements can be obtained when combining over different ROIs and different distance measures. The lowest error rate obtained is 0.210.

[1]  Joachim Hornegger,et al.  Nonrigid Registration of Joint Histograms for Intensity Standardization in Magnetic Resonance Imaging , 2009, IEEE Transactions on Medical Imaging.

[2]  Sung-Hyuk Cha,et al.  On measuring the distance between histograms , 2002, Pattern Recognit..

[3]  Christos Davatzikos,et al.  Why voxel-based morphometric analysis should be used with great caution when characterizing group differences , 2004, NeuroImage.

[4]  Stefan Klein,et al.  Early diagnosis of dementia based on intersubject whole-brain dissimilarities , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  A. Versace,et al.  Decreased entorhinal cortex volumes in schizophrenia , 2008, Schizophrenia Research.

[6]  M. Tansella,et al.  Integrating evaluative research and community-based mental health care in Verona, Italy , 2003, British Journal of Psychiatry.

[7]  Suyash P. Awate,et al.  Multivariate High-Dimensional Cortical Folding Analysis, Combining Complexity and Shape, in Neonates with Congenital Heart Disease , 2009, IPMI.

[8]  Michele Tansella,et al.  Microstructural thalamic changes in schizophrenia: a combined anatomic and diffusion weighted magnetic resonance imaging study. , 2008, Journal of psychiatry & neuroscience : JPN.

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Andrea J. van Doorn,et al.  Surface shape and curvature scales , 1992, Image Vis. Comput..

[11]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[12]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[13]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[14]  F. Deconinck,et al.  Information Processing in Medical Imaging , 1984, Springer Netherlands.

[15]  Martin Styner,et al.  Shape versus Size: Improved Understanding of the Morphology of Brain Structures , 2001, MICCAI.

[16]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[17]  M. Bellani,et al.  Thalamic‐insular dysconnectivity in schizophrenia: Evidence from structural equation modeling , 2012, Human brain mapping.

[18]  Michele Tansella,et al.  Can neuroimaging studies help us in understanding the biological causes of schizophrenia? , 2007, International review of psychiatry.

[19]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[20]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[21]  A W Toga,et al.  Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. , 2009, Schizophrenia bulletin.

[22]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[23]  Alberto Sanfeliu,et al.  Signatures versus histograms: Definitions, distances and algorithms , 2006, Pattern Recognit..

[24]  Dinggang Shen,et al.  COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements , 2007, IEEE Transactions on Medical Imaging.

[25]  W. Edelstein,et al.  A signal-to-noise calibration procedure for NMR imaging systems. , 1984, Medical physics.

[26]  Wan-Jui Lee,et al.  An experimental study on combining Euclidean distances , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[27]  M Tansella,et al.  Altered microstructure integrity of the amygdala in schizophrenia: a bimodal MRI and DWI study , 2010, Psychological Medicine.

[28]  C Barbui,et al.  Cortical white-matter microstructure in schizophrenia. Diffusion imaging study. , 2007, The British journal of psychiatry : the journal of mental science.

[29]  Haibin Ling,et al.  Diffusion Distance for Histogram Comparison , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[30]  M. Bellani,et al.  The use and meaning of the continuous performance test in schizophrenia , 2008, Epidemiologia e Psichiatria Sociale.

[31]  Kimberly M Ray,et al.  Mild cognitive impairment: apparent diffusion coefficient in regional gray matter and white matter structures. , 2006, Radiology.

[32]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[33]  Marco Rovaris,et al.  Assessment of normal-appearing white and gray matter in patients with primary progressive multiple sclerosis: a diffusion-tensor magnetic resonance imaging study. , 2002, Archives of neurology.

[34]  R. McCarley,et al.  A review of MRI findings in schizophrenia , 2001, Schizophrenia Research.

[35]  D. Rujescu,et al.  Dissecting the many genetic faces of schizophrenia , 2009, Epidemiologia e Psichiatria Sociale.

[36]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[37]  U. Castellani,et al.  Schizophrenia classification using regions of interest in brain MRI , 2009, IDA 2009.

[38]  Sun I. Kim,et al.  Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia , 2007, NeuroImage.

[39]  K. Krishnan,et al.  Diffusion tensor imaging: background, potential, and utility in psychiatric research , 2003, Biological Psychiatry.

[40]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[41]  K. Nuechterlein,et al.  Symptom dimensions in recent-onset schizophrenia and mania: a principal components analysis of the 24-item Brief Psychiatric Rating Scale , 2000, Psychiatry Research.

[42]  Robert P.W. Duin,et al.  PRTools3: A Matlab Toolbox for Pattern Recognition , 2000 .

[43]  R. Hu.  Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) , 2003 .

[44]  Mert R. Sabuncu,et al.  A Unified Framework for MR Based Disease Classification , 2009, IPMI.

[45]  Manuele Bicego,et al.  A Hybrid Generative/Discriminative Method for Classification of Regions of Interest in Schizophrenia Brain MRI , 2009 .

[46]  Michele Tansella,et al.  Information systems for mental health , 2009, Epidemiologia e Psichiatria Sociale.

[47]  L. Petrangeli,et al.  Schedules for Clinical Assessment in Neuropsychiatry , 1997, Epidemiologia e Psichiatria Sociale.

[48]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  P. Renshaw,et al.  Update on the use of MR for assessment and diagnosis of psychiatric diseases. , 2010, Radiology.

[50]  V. Calhoun,et al.  Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia , 2005, Schizophrenia Research.

[51]  Robert P. W. Duin,et al.  Dissimilarity-Based Detection of Schizophrenia , 2010, ICPR 2010.