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

Nematollah Batmanghelich, Ben Taskar, Christos Davatzikos

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)423-434
Number of pages12
JournalInformation processing in medical imaging : proceedings of the ... conference
Volume21
StatePublished - 2009
Externally publishedYes

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Machine Learning

ASJC Scopus subject areas

  • Medicine(all)

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A general and unifying framework for feature construction, in image-based pattern classification. / Batmanghelich, Nematollah; Taskar, Ben; Davatzikos, Christos.

In: Information processing in medical imaging : proceedings of the ... conference, Vol. 21, 2009, p. 423-434.

Research output: Contribution to journalArticle

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