Abstract: Systems and methods are provided for analyzing a series of images. A contour initializer establishes a plurality of initial contours from respective images within the series of images. A model initializer interconnects the plurality of initial contours into a surface model. A model refiner refines the plurality of initial contours by manipulating the surface model.

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.