Automatic detection and monitoring of nodules and shaped targets in image data

Abstract

A method for detecting a nodule in image data including the steps of segmenting scanning information from an image slice to isolate lung tissue from other structures, resulting in segmented image data; extracting anatomic structures, including any potential nodules, from the segmented image data, resulting in extracted image data; and detecting possible nodules from the extracted image data, based on deformable prototypes of candidates generated by a level set method in combination with a marginal gray level distribution method. Embodiments of the invention also relate to an automatic method for detecting and monitoring a nodule in image data, where the method includes the steps of determining adaptive probability models of visual appearance of small 2D and large 3D nodules to control evolution of deformable models to get accurate segmentation of pulmonary nodules from image data; modeling a first set of nodules in image data with a translation and rotation invariant Markov-Gibbs random field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training nodules; modeling a second subsequent set of nodules in image data by estimating a linear combination of discrete Gaussians; and integrating both models to guide the evolution of the deformable model to determine and monitor the boundary of each detected nodule in the image data.