Classification in medical image analysis using adaptive metric k-NN

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationMedical Imaging 2010 : image processing
EditorsBenoit M. Dawant, David R. Haynor
Number of pages9
PublisherSPIE - International Society for Optical Engineering
Publication date2010
Article number76230S
ISBN (Electronic)9780819480248
Publication statusPublished - 2010
EventSPIE Medical Imaging 2010 - San Diego, United States
Duration: 13 Feb 201018 Feb 2010
Conference number: 2010


ConferenceSPIE Medical Imaging 2010
LandUnited States
BySan Diego
SeriesProgress in Biomedical Optics and Imaging

ID: 18229907