A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection

Purpose This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes. Methods The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules. Results The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation. Conclusion The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.