Three-dimensional approach to lung nodule detection in helical CT

We are developing an automated method for the detection of lung nodules in helical computed tomography (CT) images. This technique incorporates both two-dimensional and three-dimensional analyses to exploit the volumetric image data acquired during a CT examination. Gray-level thresholding is used to segment the lungs within the thorax. A rolling ball algorithm is applied to more accurately define the segmented lung regions. The set of segmented CT sections, which represents the complete lung volume, is iteratively thresholded, and a 10-point connectivity scheme is used to identify contiguous three-dimensional structures. Structures with volumes less than a predefined maximum value comprise the set of nodule candidates, which is then subjected to two- and three-dimensional feature analysis. To distinguish between candidates representing nodule and non-nodule structures, the values of the features are merged through linear discriminant analysis. When applied to a database of 17 helical thoracic CT cases, gray-level thresholding combined with the volume criterion detected 82% of the lung nodules. Linear discriminant analysis yielded an area under the receiver operating characteristic (ROC) curve of 0.93 in the task of distinguishing between nodule and non-nodule structures within this set of nodule candidates.