A method of pulmonary nodule detection utilizing multiple support vector machines

It has been proven that early detection of pulmonary nodules is an important clinical indication for early-stage lung cancer diagnosis. Recently, support vector machines(SVMs) have been extensively used in pattern recognition. However, the application object for SVMs used for false positives(FPs) reduction when detecting lung nodules is generally based on only axial plane. In this paper, we propose a computerized system aimed at lung nodules detection in Multi-Slice Computed Tomography(MSCT) scans with multiple SVMs; it segments the lung field, extracts three sets of candidates regions with two dimensional(2D) dot-enhancement filter on three slice directions respectively, reduces the FPs with multiple SVMs, and then, integrates the classification results by using pixel analysis and region growing method. The proposed scheme is applied on two lung CT datasets. The experimental results illustrate the efficiency of the proposed method.