Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels

• A new 3D, CAD for lung nodule candidates was introduced and compared with other works.
• Multiscale dot enhancement filter and Laplacian of Gaussian kernels were combined.
• Relatively a large number of FPs was reduced before using feature based classification.
• The performance of the proposed method was evaluated with 42 independent images from LIDC.
• The scheme detects nodules with wide variations in size, shape, intensity and location.

This work is focused on application of a new technique in the first steps of computer-aided detection (CAD) of lung nodules. The scheme includes segmenting the lung volume and detecting most of the nodules with a low number of false positive (FP) objects. The juxtapleural nodules were properly included and the airways excluded in the lung segmentation. Among the suspicious regions obtained from the multiscale dot enhancement filter, those containing the center of nodule candidates, were determined. These center points were achieved from a 3D blob detector based on Laplacian of Gaussian kernels. Then the volumetric shape index (SI) that encodes the 3D local shape information was calculated for voxels in the determined regions. The performance of the scheme was evaluated by using 42 CT images from the Lung Image Database Consortium (LIDC). The results show that the average number of FPs reaches to 38.8 per scan with the sensitivity of 95.9% in the initial detections. The scheme is adaptable to detect nodules with wide variations in size, shape, intensity and location. Comparison of results with previously reported ones indicates that the proposed scheme can be satisfactory applied for initial detection of lung nodules in the chest CT images.