LUNG NODULE DETECTION AND CLASSIFICATION

摘要

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. Thereafter, the computer performs a pixel similarity analysis on the appropriate regions within the CT images to detect potential nodules and performs one or more expert analysis techniques using the features of the potential nodules to determine whether each of the potential nodules is or is not a lung nodule. Thereafter, the computer uses further features, such as speculation features, growth features, etc. in one or more expert analysis techniques to classify each detected nodule as being either benign or malignant. The computer then displays the detection and classification results to the radiologist to assist the radiologist in interpreting the CT exam for the patient.