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

作者：Saien Soudeh^*; Moghaddam Hamid Abrishami; Fathian Mohsen

来源：International Journal of Computer Assisted Radiology and Surgery, 2018, 13(3): 397-409.

DOI：10.1007/s11548-017-1656-8

• Lung nodules detection
• False positives reduction
• Imbalanced data classification
• Sparse field method
• RUSBoost classifier

This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes.
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.
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.
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.

• 出版日期2018-3