A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification

Authors:
Nematollah Batmanghelich;Ben Taskar;Christos Davatzikos
Affiliations:
Section of Biomedical Image Analysis, Raddiology Department, University of Pennsylvania, Philadelphia, USA PA 19014;Computer and Information Department, University of Pennsylvania, Philadelphia, USA PA 19104;Section of Biomedical Image Analysis, Raddiology Department, University of Pennsylvania, Philadelphia, USA PA 19014
Venue:
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Year:
2009

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Abstract

This paper presents a general and unifying optimization framework for the problem of feature extraction and reduction for high-dimensional pattern classification of medical images. Feature extraction is often an ad hoc and case-specific task. Herein, we formulate it as a problem of sparse decomposition of images into a basis that is desired to possess several properties: 1) Sparsity and local spatial support, which usually provides good generalization ability on new samples, and lends itself to anatomically intuitive interpretations; 2) good discrimination ability, so that projection of images onto the optimal basis yields discriminant features to be used in a machine learning paradigm; 3) spatial smoothness and contiguity of the estimated basis functions. Our method yields a parts-based representation, which warranties that the image is decomposed into a number of positive regional projections. A non-negative matrix factorization scheme is used, and a numerical solution with proven convergence is used for solution. Results in classification of Alzheimers patients from the ADNI study are presented.