Highly Robust Statistical Methods in Medicai Image Analysis

Standard multivariate statistical methods in medicai applications are too sensitive to the assumption of multivariate normality and the presence of outliers in the data. This paper is devoted to robust statistical methods. In the context of medical image analysis they allow to solve the tasks of face detection and face recognition in a database of images. The results of the robust approaches in image analysis turn out to outperform those obtained with standard methods. Robust methods also have desirable properties appealing for practical applications, including dimension reduction and clear interpretability.

[1]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[2]  Anuj Srivastava,et al.  Statistical Shape Analysis , 2014, Computer Vision, A Reference Guide.

[3]  Pavel íek,et al.  Semiparametrically weighted robust estimation of regression models , 2011 .

[4]  Jan Kalina,et al.  Some Diagnostic Tools in Robust Econometrics , 2011 .

[5]  C. Cacou Anthropometry of the head and face , 1995 .

[6]  Jan de Leeuw,et al.  Robust Statistical Methods With R , 2006, Technometrics.

[7]  Henry W. Altland,et al.  Applied Functional Data Analysis , 2003, Technometrics.

[8]  A. Zankl Computer-Aided Anthropometry in the Evaluation of Dysmorphic Children , 2004, Pediatrics.

[9]  Mia Hubert,et al.  ROBPCA: A New Approach to Robust Principal Component Analysis , 2005, Technometrics.

[10]  Jana Zvárová,et al.  Biomedical Informatics Research for Individualized Life-long Shared Healthcare , 2009 .

[11]  J. Kalina Facial Symmetry in Robust Anthropometrics * , 2012, Journal of forensic sciences.

[12]  Ricardo A. Maronna,et al.  Robust Statistical Methods , 2011, International Encyclopedia of Statistical Science.

[13]  Magnus Enquist,et al.  Symmetry, beauty and evolution , 1994, Nature.

[14]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[15]  P. L. Davies,et al.  Breakdown and groups , 2005, math/0508497.

[16]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[17]  Stephen M. Stigler,et al.  The Changing History of Robustness , 2010 .

[18]  Katrien van Driessen,et al.  A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.

[19]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[20]  G. N. Vark,et al.  Multivariate Statistical Methods in Physical Anthropology , 1984 .

[21]  P. Filzmoser,et al.  Algorithms for Projection-Pursuit Robust Principal Component Analysis , 2007 .

[22]  Jan Kalina,et al.  Locating landmarks using templates , 2007 .

[23]  V. Moulin,et al.  Abstract , 2004, Veterinary Record.

[24]  S. Stigler Francis Galton's Account of the Invention of Correlation , 1989 .

[25]  Christoph von der Malsburg,et al.  Computer-based recognition of dysmorphic faces , 2003, European Journal of Human Genetics.

[26]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Mia Hubert,et al.  Fast and robust discriminant analysis , 2004, Comput. Stat. Data Anal..

[28]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[29]  F. Bookstein,et al.  Morphometric Tools for Landmark Data: Geometry and Biology , 1999 .

[30]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[31]  F. Galton I. Co-relations and their measurement, chiefly from anthropometric data , 1889, Proceedings of the Royal Society of London.

[32]  Pavel Cízek,et al.  Semiparametrically weighted robust estimation of regression models , 2011, Comput. Stat. Data Anal..

[33]  Jan Ámos Vísek,et al.  Consistency of the least weighted squares under heteroscedasticity , 2011, Kybernetika.

[34]  Fred L. Bookstein,et al.  Morphometric Tools for Landmark Data. , 1998 .

[35]  L. Nelson,et al.  Epidemiology of childhood tuberculosis in the United States, 1993-2001: the need for continued vigilance. , 2004, Pediatrics.

[36]  Jan Kalina Robust Image Analysis in the Evaluation of Gene Expression Studies , 2010, ERCIM News.

[37]  Tobias Vollmar,et al.  Syndrome identification based on 2D analysis software , 2006, European Journal of Human Genetics.

[38]  C. Croux,et al.  Principal Component Analysis Based on Robust Estimators of the Covariance or Correlation Matrix: Influence Functions and Efficiencies , 2000 .

[39]  Stephane Heritier,et al.  Robust Methods in Biostatistics , 2009 .