Computerized detection of pulmonary nodules using cellular neural networks in CT images

Xiangwei Zhang; Geoffrey McLennan M.D.; Eric A. Hoffman; Milan Sonka

doi:10.1117/12.535556

12 May 2004 Computerized detection of pulmonary nodules using cellular neural networks in CT images

Xiangwei Zhang, Geoffrey McLennan M.D., Eric A. Hoffman, Milan Sonka

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535556
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) non-pleural pulmonary nodules in high resolution helical CT scans. A new 3D automated scheme using cellular neural networks is presented. Different from most previous methods, this scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. A 3D discrete-time cellular neural network (DTCNN) was constructed to give a reliable voxel classification by collecting information in a neighborhood. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work, with 2 scans forming the training set, and the remaining 17 cases being the testing set. The evaluation was composed of two stages. During the first stage, a pulmonologist and our CAD system independently detected nodules in the testing set. Then, the suspected nodule areas located by the computer were reviewed by the pulmonologist to confirm nodules missed by the human in the first review. There were 32 true nodules detected by the computer but missed by the pulmonologist in the first review, in which 30 non-juxtapleural nodules were found. Considering the nodules detected by the pulmonologist during the first and second reviews as the truth, 52 of 62 non-pleural nodules were detected by the CAD system (sensitivity being 83.9%), with the number of false positives being 3.47 per case.

Citation Download Citation

Xiangwei Zhang, Geoffrey McLennan M.D., Eric A. Hoffman, and Milan Sonka "Computerized detection of pulmonary nodules using cellular neural networks in CT images", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535556

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