Purpose: Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method.
Methods: A multistage segmentation model is presented in this study. The lung region is extracted by applying corner-seeded region growing combined with differential evolution-based optimal thresholding. In addition to this, morphological operations are applied in boundary smoothing, hole filling and juxtavascular nodule extraction. Geometric properties along with 3D edge information are applied to extract nodule candidates. Geometric texture features descriptor (GTFD) followed by support vector machine-based ensemble classification is employed to distinguish actual nodules from the candidate set.
Results: A publicly available dataset, namely lung image database consortium and image database resource initiative, is used to evaluate performance of the proposed method. The classification is performed over GTFD feature vector and the results show 99% accuracy, 98.6% sensitivity and 98.2% specificity with 3.4 false positives per scan (FPs/scan).
Conclusion: A lung nodule detection method is presented to facilitate radiologists in accurately diagnosing cancer from CT images. Results indicate that the proposed method has not only reduced FPs/scan but also significantly improved sensitivity as compared to related studies.
Keywords: Ensemble learning; Hybrid features; Lung nodules; Segmentation; Support vector machine.