- Patent Title: Method for adaptive computer-aided detection of pulmonary nodules in thoracic computed tomography images using hierarchical vector quantization and apparatus for same
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Application No.: US14767780Application Date: 2014-02-14
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Publication No.: US09639933B2Publication Date: 2017-05-02
- Inventor: Jerome Zheng Liang , William H. Moore , FangFang Han , Bowen Song , Huafeng Wang
- Applicant: The Research Foundation for The State University of New York
- Applicant Address: US NY Albany
- Assignee: The Research Foundation for The State University of New York
- Current Assignee: The Research Foundation for The State University of New York
- Current Assignee Address: US NY Albany
- Agency: The Farrell Law Firm, P.C.
- Agent John F. Gallagher, III
- International Application: PCT/US2014/016461 WO 20140214
- International Announcement: WO2014/127224 WO 20140821
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06K9/46 ; G06T7/40 ; G06T7/60 ; G06T11/00

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.
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