Classification in medical image analysis using adaptive metric k-NN
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Classification in medical image analysis using adaptive metric k-NN. / Chen, Chen; Chernoff, Konstantin; Karemore, Gopal; Lo, Pechin Chien Pau; Nielsen, Mads; Lauze, Francois Bernard.
Medical Imaging 2010: image processing. ed. / Benoit M. Dawant; David R. Haynor. SPIE - International Society for Optical Engineering, 2010. 76230S (Progress in Biomedical Optics and Imaging; No. 33, Vol. 11).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Classification in medical image analysis using adaptive metric k-NN
AU - Chen, Chen
AU - Chernoff, Konstantin
AU - Karemore, Gopal
AU - Lo, Pechin Chien Pau
AU - Nielsen, Mads
AU - Lauze, Francois Bernard
N1 - Conference code: 2010
PY - 2010
Y1 - 2010
N2 - The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.
AB - The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.
U2 - 10.1117/12.844338
DO - 10.1117/12.844338
M3 - Article in proceedings
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2010
A2 - Dawant, Benoit M.
A2 - Haynor, David R.
PB - SPIE - International Society for Optical Engineering
Y2 - 13 February 2010 through 18 February 2010
ER -
ID: 18229907