Emprego de Equações Booleanas e Vetor de Filtros na Detecção de Nódulos Pulmonares em Tomografias Computadorizadas do Tórax

Abstract

Detection of lung cancer in the early stages significantly increases the chances of survival. 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 reference, our method reaches the highest sensitivity levels among listed results. Even adopting a “ground truth” which hinders nodule detection and false positive reduction tasks, our results are in line with recent literature standards showing the method is able to detect lung nodules effectively. The major contributions of this thesis are the inclusion in the methods of lesions disregarded by reference standards that consider only the opinion of the majority of specialists.