Abstract: A system for context-sensitive medical communication is described. Patient presentation data is obtained, the patient presentation data is mapped to biological system data, wherein the biological system data are obtained by a population-based comparison, and a relevance-driven summary is generated. Following the primary read, the study can be compressed and transmitted remotely, such as in teleconsultation described below. The imaging study can be provided by patient presentation mapping to medical nomenclature, and mapping the patient study to an appropriate normalized atlas which has been created by averaging and morphing as well as quantification and providing labels which have come from data mining of reports.

Abstract: A method and system are described that generate customizable reference atlases by automatically extracting relevant images from imaging studies of similar patients stored in an atlas database of archived imaging studies. Keying off user input as to the characteristics of the target patient currently under examination, the method and system identify archived volumes of patients images having similar characteristics, identifies relevant images from those collections, and processes those image to equate their intensity, contrast, and/or orientation with relevant target patient images. In addition, the disclosed method and system automatically extract relevant images from the patient's MR and CT imaging studies. Expert rules are used to infer the suspected abnormality and the anatomical location from structured input related to patient presenting condition.

Abstract: A method and system are described that identify anatomical abnormalities in internal images of a subject under study. The method and system use principal component analysis of a subject's image as compared to a training set of images. The training set of images incorporates both normal and abnormal cases. Specifically the principal component analysis identifies key image slices to pinpoint image slices whose vectorized and transformed representations quantitatively diverge from training set images identified as normal and/or resemble training set images identified as abnormal. The method and system automatically classifies images as normal or abnormal based upon the content of the images, and/or automatically provides comparable reference images for aiding physicians in reaching a diagnosis.