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Automated detection of lung nodules in CT scans: Preliminary results
Author(s) -
Armato Samuel G.,
Giger Maryellen L.,
MacMahon Heber
Publication year - 2001
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1387272
Subject(s) - medical imaging , radiology , medicine , lung , computed tomography , nuclear medicine , medical physics
We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two‐dimensional and three‐dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section‐by‐section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray‐level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18‐point connectivity scheme is used to identify contiguous three‐dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray‐level features are computed for each nodule candidate. After a rule‐based approach is applied to greatly reduce the number of nodule candidates that corresponds to non‐nodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false‐positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave‐one‐out‐by‐case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false‐positive detections per section when applied to the complete 43‐case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false‐positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.