Abstract: A method for providing a medical service in a therapeutic area requested by a client. In one embodiment, the method includes the step of defining a plurality of offerings associated with the medical service in the therapeutic area. At least one service category is associated with each of the offerings. At least one activity is associated with each of the service categories. At least one tool is associated with each of the activities. The method further includes the step of associating the offerings, the activities, the service categories and the tools with a respective plane of a hypercube. In response to the medical service, at least one location in the hypercube, which represents the offering, service category, activity and tool required to provide the medical service, is identified.

Abstract: An automated method and system for autocontouring organs and other anatomical structures in CT and other medical images employs one or more contouring techniques, depending on the particular organs or structures to be contoured. In a preferred embodiment, an edge-based technique is employed to contour one or more organs. A multiple hypothesis testing technique can be employed to improve the accuracy of the resulting contour. Independent algorithms can be employed for contouring multiple organ in a given region, such as the male pelvic region. An integration algorithm can be employed to combine the results of the independent algorithms to improve accuracy further.

Abstract: An automated method and system for detecting lung nodules from thoracic CT images employs an image processing algorithm (22) consisting of two main modules: a detection module (24) that detects nodule candidates from a given lung CT image dataset, and a classifier module (26), which classifies the nodule candidates as either true or false to reject false positives amongst the candidates. The detection module (24) employs a curvature analysis technique, preferably based on a polynomial fit, that enables accurate calculation of lung border curvature to facilitate identification of juxta-pleural lung nodule candidates, while the classification module (26) employs a minimal number of image features (e.g., 3) in conjunction with a Bayesian classifier to identify false positives among the candidates.