A surface-based approach for classification of 3D neuroanatomic structures

  • Authors:

  • Li Shen;James Ford;Fillia Makedon;Andrew Saykin

  • Affiliations:

  • (Correspd. Tel.: +1 508 910 6691/ Fax: +1 508 999 9144/ lshen@umassd.edu) Dartmouth Exp. Vis. Lab., Dept. of Comp. Sci., Dartmouth Coll., Hanover, NH 03755, USA and Dept. of Comp. and Info. Sci., ...;Dartmouth Experimental Visualization Laboratory, Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA. E-mail: {li, jford, makedon}@cs.dartmouth.edu;Dartmouth Experimental Visualization Laboratory, Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA. E-mail: {li, jford, makedon}@cs.dartmouth.edu;Brain Imaging Laboratory, Departments of Psychiatry and Radiology, Dartmouth Medical School, Lebanon, NH 03756, USA. E-mail: saykin@dartmouth.edu

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2004

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Abstract

We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.