A new computer algorithm is presented to distinguish a special, most probably benign, subclass of lung nodules called perifissural opacities (PFO?s), from potentially malignant nodules. The method focuses on the quantification of two characteristic properties of PFO?s, namely the typical flattened surface of the nodule and its attachment to plate-like structures in the direct neighborhood of the nodule (the lung fissures). For the detection of fissures in the proximity of the nodule, an analysis based on the eigenvalues of the Hessian matrix has been developed. Further processing with a voxel grouping algorithm is shown to substantially improve the results of the fissure detection. Through a comparison of Hough transforms of the nodule boundary and the detected fissure voxels, features are constructed that enable a reliable separation of benign PFO from other lesions.