TY - GEN
T1 - A general and unifying framework for feature construction, in image-based pattern classification
AU - Batmanghelich, Nematollah
AU - Taskar, Ben
AU - Davatzikos, Christos
PY - 2009/9/21
Y1 - 2009/9/21
N2 - 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.
AB - 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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U2 - 10.1007/978-3-642-02498-6_35
DO - 10.1007/978-3-642-02498-6_35
M3 - Conference contribution
C2 - 19694282
AN - SCOPUS:70349336340
SN - 3642024971
SN - 9783642024979
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 423
EP - 434
BT - Information Processing in Medical Imaging - 21st International Conference, IPMI 2009, Proceedings
T2 - 21st International Conference on Information Processing in Medical Imaging, IPMI 2009
Y2 - 5 July 2009 through 10 July 2009
ER -