A novel method for lung nodule detection in computed tomography scans based on Boolean equations and vector of filters techniques. (May 2022) - de Mesquita, Vítor A.; Cortez, Paulo C.; Ribeiro, Alyson B.N.; de Albuquerque, Victor Hugo C.
Abstract: This work presents a novel method that uses a Vector of Pre-processing Filters combined with simple relational and Boolean equations for pulmonary nodule detection. To isolate nodules from other lung structures, we propose a 16 filter scheme endowed with multiscale median masks, statistical-based thresholds, and 3-D morphological operations. In the Boolean False Positive Reduction stage, relational and Boolean equations select nodule candidates from pre-processing filters using our descriptor with 20 attributes. Finally, a Convolutional Neural Network (CNN) classifies the remaining structures into nodule or no nodule. The method reached 92.75% sensitivity for an average of 8 false positives per exam using all exams in the public Lung Image Database Consortium (LIDC) with a slice thickness of less than 2 mm that contains lesions larger than 3 mm marked "nodule" by at least 3 radiologists. For a less consensual gold standard, our method reaches the highest sensitivity levels among listed results. Highlights: The results confirm the superior effectiveness of the proposed method. Highest accuracy, precision and specificity with the lowest standard deviation. The results confirm the superior effectiveness of the proposed method.