Abstract: A method for the automated segmentation of in vivo image data is disclosed. A region of carotid artery in a number of patients was imaged using MRI. Histological data for each imaged region was then obtained, identifying various atherosclerotic plaque components in the imaged region. A portion of the histological data, and the image data, was used to generate PDFs based on image intensity, and on morphological data (local wall thickness and distance from lumen). The remaining data was used to validate the method. A plurality of MRI images were taken at various weightings, and the images were registered and normalized. The lumen and outer wall boundary were identified. The PDFs were combined in a Bayesian analysis with the intensity and morphological data to calculate the likelihood that each pixel corresponded to each of four plaque components. A contour algorithm was applied to generate contours segmenting the images by composition.

Abstract: A method and system for in-vivo characterization of lesion feature is disclosed. Using a non-invasive medical imaging apparatus, an image of an interior region of a patient's body is obtained. The interior region may include lesion feature (such as plaques) components from a list of components. The lesion feature components are identified by classifying each point in the image as either corresponding to one of the lesion feature components in the list of components or not, using image intensity information and image morphology information, a first relationship (such as an intensity score) correlating image intensity information with the components in the list of components and a second relationship (such as a morphology score) correlating image morphology information with the components in the list of components. Further, a variety of lesion feature characteristics is derived from the result of the classification.

Abstract: A novel technique directed toward risk assessment of a patient's plaque vulnerability, wherein clinical events may be caused by internal plaque components affecting a lumen within an artery. A surface area projection or shadow of one or more plaque components onto a lumen can be measured and assessed. Optionally, a total volume projection onto the lumen can also be measured and assessed to refine the determination of risk to a patient and to monitor the progression of atherosclerosis over time.

Abstract: A method for the automated segmentation of in vivo image data is disclosed. A region of carotid artery in a number of patients was imaged using MRI. Histological data for each imaged region was then obtained, identifying various atherosclerotic plaque components in the imaged region. A portion of the histological data, and the image data, was used to generate PDFs based on image intensity, and on morphological data (local wall thickness and distance from lumen). The remaining data was used to validate the method. A plurality of MRI images were taken at various weightings, and the images were registered and normalized. The lumen and outer wall boundary were identified. The PDFs were combined in a Bayesian analysis with the intensity and morphological data to calculate the likelihood that each pixel corresponded to each of four plaque components. A contour algorithm was applied to generate contours segmenting the images by composition.