Hybrid CAD scheme for lung nodule detection in PET/CT images

Lung cancer is the leading cause of death among male in the world. PET/CT is useful for the detection of early lung cancer since it is an imaging technique that has functional and anatomical information. However, radiologist has to examine using the large number of images. Therefore reduction of radiologist's load is strongly desired. In this study, hybrid CAD scheme has been proposed to detect lung nodule in PET/CT images. Proposed method detects the lung nodule from both CT and PET images. As for the detection in CT images, solitary nodules are detected using Cylindrical Filter that we developed. PET images are binarized based on standard uptake value (SUV); highly uptake regions are detected. FP reduction is performed using seven characteristic features and Support Vector Machine. Finally by integrating these results, candidate regions are obtained. In the experiment, we evaluated proposed method using 50 cases of PET/CT images obtained for the cancer-screening program. We evaluated true-positive fraction (TPF) and the number of false positives / case (FPs/case). As a result, TPFs for CT and PET were 0.67 and 0.38, respectively. By integrating the both results, TPF was improved to 0.80. These results indicate that our method may be useful for the lung cancer detection using PET/CT images.