Texture-based continuous probabilistic framework for medical image representation and classification

This paper addresses the problem of medical image representation and classification. A texture-based continuous probabilistic framework is presented, according to which images taken at different angles are represented using several probabilistic models connected in parallel. Classification of the images is performed using a parallel Gaussian mixture models (GMMs) framework, which is composed of several GMMs, schematically connected in parallel, where each GMM represents a different imaging angle. The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 100 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.