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29 April 2005 A complete CAD system for pulmonary nodule detection in high resolution CT images
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Abstract
The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) pulmonary nodules in high resolution helical CT scans. A new CAD system is proposed to locate both juxtapleural nodules and non-pleural nodules. Isotropic resampling and lung segmentation are performed as preprocessing steps. Morphological closing was utilized to smooth the lung contours to include the indented possible juxtapleural locations, thresholding and 3D component analysis were used to obtain 3D volumetric nodule candidates; furthermore, gray level and geometric features were extracted, and analyzed using linear discriminant analysis (LDA) classifier. Leave one case out method was used to evaluate the LDA. To deal with non-pleural nodules, a discrete-time cellular neural network (DTCNN) based on local shape features was developed. 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. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. Nonoverlapping training and testing sets were utilized in the non-pleural nodule detection. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work. The juxtapleural nodule detection method was able to obtain sensitivity 81.25% with an average of 8.29 FPs per case. The non-pleural nodule finding scheme attained sensitivity of 83.9% with an average 3.47 FPs/case. Combining the two subsystems together, an overall performance of 82.98% sensitivity with 11.76 FPs/case can be obtained.
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Xiangwei Zhang, Geoffrey McLennan, Eric A. Hoffman, and Milan Sonka "A complete CAD system for pulmonary nodule detection in high resolution CT images", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594916
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