An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification

Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfuss

Abstract: This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the unscented-transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.