Abstract: Provided are an apparatus and method for fast and adaptive computer-aided detection of pulmonary nodules and differentiation of malignancy from benignancy in thoracic CT images using a hierarchical vector quantization scheme. Anomalous pulmonary nodules are detected by obtaining a two-dimensional (2D) feature model of a pulmonary nodule, segmenting the pulmonary nodule by performing vector quantification to expand the 2D feature model to a three-dimensional (3D) model, and displaying image information representing whether the pulmonary nodule is benign, based upon the 3D model expanded from the 2D feature model, with duplicate information eliminated by performing feature reduction performed using a principal component analysis and a receiver operating characteristics area under the curve merit analysis.

Abstract: Provided are an apparatus and method for fast and adaptive computer-aided detection of pulmonary nodules and differentiation of malignancy from benignancy in thoracic CT images using a hierarchical vector quantization scheme. Anomalous pulmonary nodules are detected by obtaining a two-dimensional (2D) feature model of a pulmonary nodule, segmenting the pulmonary nodule by performing vector quantification to expand the 2D feature model to a three-dimensional (3D) model, and displaying image information representing whether the pulmonary nodule is benign, based upon the 3D model expanded from the 2D feature model, with duplicate information eliminated by performing feature reduction performed using a principal component analysis and a receiver operating characteristics area under the curve merit analysis.

Abstract: A computer-based method for diagnosing a region of interest within an anatomical structure having the steps of receiving a 3D volumetric representation of the anatomical structure, and identifying at least one volume of interest and volume of normal of the anatomical structure. A first feature set can be generated based on a density, a gradient and a curvature of the volume of interest, and the first feature set can be compared to a second feature set to diagnose the region of interest to at least one a plurality of pathology types.