Classification in medical image analysis using adaptive metric k-NN

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
TitelMedical Imaging 2010 : image processing
RedaktørerBenoit M. Dawant, David R. Haynor
Antal sider9
ForlagSPIE - International Society for Optical Engineering
ISBN (Elektronisk)9780819480248
StatusUdgivet - 2010
BegivenhedSPIE Medical Imaging 2010 - San Diego, USA
Varighed: 13 feb. 201018 feb. 2010
Konferencens nummer: 2010


KonferenceSPIE Medical Imaging 2010
BySan Diego
NavnProgress in Biomedical Optics and Imaging

ID: 18229907