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Automatic detection of endobronchial lesions using virtual bronchoscopy: comparison of two methods

3D reconstruction of medical images is increasingly being used to diagnose disease and to direct therapy. Virtual bronchoscopy is a recently developed type of 3D reconstruction of the airways that may be useful for diagnosis of lesions of the airway. In this study, we compare two methods for computer-aided diagnosis of polypoid airway tumors: a parametric (`patch') and non-parametric ('grey-scale') algorithm. We found that both methods have comparable specificities. Although the non-parametric method is twelve times faster than the parametric method, we found that is sensitivity lags behind that of the parametric method by 3 to 16% when lesions of all sizes are considered. For lesions at least 5 mm in size, the sensitivities are comparable if a small convolution kernel is used.