Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform

Different frequency components of the lung, which have not been fully considered in traditional computer-aided detection systems for pulmonary nodules, can cause heterogeneous energy distribution. Hence, spectral analysis, which is an important time–frequency representation tool, is utilized to characterize the frequency-dependent energy responses of nodules. In this study, a novel spectral-analysis-based method for nodule candidate detection is presented. The optimal fractional S-transform is applied to transform raw computed tomography images from the spatial to time–frequency domain. Next, a time–frequency cube is decomposed using spectral decomposition to a frequency-dependent energy slice. Subsequently, an energy distribution is obtained by the Teager–Kaiser energy (TKE) to characterize the nodules. Finally, nodule candidates are detected using rule-based and threshold algorithms in the TKE image. The proposed method is validated on a clinical CT data set from Sichuan Provincial People's Hospital. The signal-to-clutter ratio (SCR) increases by 35.5% with respect to raw CT slices. Furthermore, the proposed method exhibits a sensitivity of 97.87%, with only 6.8 false positives per slice. The total number of nodule candidates has an average reduction of 50%. The results indicate that the time–frequency features can effectively characterize solid nodules. Moreover, the proposed method demonstrates accurate detection and can reduce the number of false positive efficiently.