A general and unifying framework for feature construction, in image-based pattern classification.

Section of Biomedical Image Analysis, Radiology Department, University of Pennsylvania, Philadelphia, PA 19014, USA.
Information processing in medical imaging: proceedings of the ... conference 02/2009; 21:423-34. DOI: 10.1007/978-3-642-02498-6_35
Source: PubMed

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.

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