Abstract: A method for detecting bone and bone disease using MRI images includes: detecting and segmenting bone borders using dark bone border intensity information from an MRI image; and detecting bone disease within a segmented image region.

Abstract: A method for segmenting tubular structures in medical images includes providing at least a start point and an end point in a digital image volume, minimizing an action surface U0(p) which, at each image point p, corresponds to a minimal energy integrated along a path that starts at start point p0 and ends at p, sliding back on the minimal action surface from an end point to the start point to find a minimal path connecting the terminal points, initializing a level set function with points on the minimal path, and evolving the level set function to find a surface of a structure about the minimal path, wherein the level set function is constrained to be close to a signed distance function and wherein the level set function is prevented from growing wider than a predetermined diameter R, wherein the surface about the minimal path defines a tubular structure.

Abstract: A system and method for toboggan-based object detection in cutting planes are provided. A method for detecting an object in an image includes: determining a region of interest (ROI) in the image; determining a toboggan potential for each image element in the ROI; extracting a plurality of cutting planes from the ROI; and performing a tobogganing in the cutting planes to form a toboggan cluster to determine a location of the object, wherein image elements inside the toboggan cluster are stored in a cluster-member list, image elements on an outer-border of the toboggan cluster are stored in an outer-border list and image elements on an inner-border of the toboggan cluster are stored in an inner-border list.

Abstract: A system and method for determining the presence or absence of candidate objects in a digital image by computing a gradient field of the digital image and then applying predetermined filters to the gradient field to obtain a response image which can be further processed to detect the objects. The application of these filters to the digital image can be performed by convolution and the filters can be adapted based on the shape of the object to be searched.

Abstract: CAD (computer-aided diagnosis) systems and applications for cardiac imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heart-related medical conditions, and other automated decision support functions. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.