Automated lung nodule detection and segmentation

Abstract

A computer-aided detection (CAD) system for lung nodules in CT scans was developed. For the detection of lung nodules two different methods were applied and only pixels which were detected by both methods are marked as true positives. The first method uses a multi-threshold algorithm, which detect connected regions within the lung that have an intensity between specified threshold values. The second is a multi-scale detection method. The data are searched for points located in spherical objects. The image data were smoothed with a 3D Gaussian filter and computed the Hessian matrix and eigenvectors and eigenvalues for all pixels detected by the first algorithm. By analyzing the eigenvalues points that lie within a spherical structure can be located. For segmentation of the detected nodules an active contour model was used. A two-dimensional active contour with four energy terms describing form and position of the contour in the image data was implemented. In addition balloon energy to get the active contour was used growing out from one point. The result of our detection part is used as input for the segmentation part. To test the detection algorithms we used 19 CT volume data sets from a low-dose CT studies. Our CAD system detected 58% of the nodules with a falsepositive rate of 1.38. Additionally we take part at the ANODE09 study whose results will be presented at the SPIE meeting in 2009.