Method for adaptive computer-aided detection of pulmonary nodules in thoracic computed tomography images using hierarchical vector quantization and apparatus for same

US9639933B2 · kind B2 · utility

Assignee

• The Research Foundation for The State University of New York · US

Inventors

• Jerome Z. Liang · Stony Brook, US
• William H. Moore · Fremont, US
• FangFang Han · East Setauket, US
• Bowen Song · Midland School, US
• Huafeng Wang · Beijing, CN

Key dates

• Filing date: Feb 14, 2014
• Grant date: May 2, 2017
• Expiry date: Apr 25, 2034

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30064
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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. A textural feature analysis detects an anomalous pulmonary nodule, and 2D texture features are calculated from 3D volumetric data to provide improved gain compared to calculation from a single slice of 3D data.