Abstract: A computer assisted method of detecting and classifying lung nodules within a set of CT images to identify the regions of the CT images in which to search for potential lung nodules. The lungs are processed to identify a subregion of a lung on a CT image. The computer defines a nodule centroid for a nodule class of pixels and a background centroid for a background class of pixels within the subregion in the CT image; and determines a nodule distance between a pixel and the nodule centroid and a background distance between the pixel and the background centroid. Thereafter, the computer assigns the pixel to the nodule class or to the background class based on the first and second distances; stores the identification in a memory; and analyzes the nodule class to determine the likelihood of each pixel cluster being a true nodule.

Abstract: A computer assisted method of detecting and classifying lung nodules within a set of CT images includes performing body contour, airway, lung and esophagus segmentation to identify the regions of the CT images in which to search for potential lung nodules. The lungs are processed to identify the left and right sides of the lungs and each side of the lung is divided into subregions including upper, middle and lower subregions and central, intermediate and peripheral subregions. The computer analyzes each of the lung regions to detect and identify a three-dimensional vessel tree representing the blood vessels at or near the mediastinum. The computer then detects objects that are attached to the lung wall or to the vessel tree to assure that these objects are not eliminated from consideration as potential nodules.