Pulmonary adenocarcinoma characterization using computed tomography images (2019)
Lung cancer is one of the pathologies that sensitively affects the health of human beings. Particularly, the pathology called pulmonary adenocarcinoma represents 25% of all lung cancers. In this research, we propose a semiautomatic technique for the characterization of a tumor (adenocarcinoma type), present in a three-dimensional pulmonary computed tomography dataset. Following the basic scheme of digital image processing, first, a bank of smoothing filters and edge detectors is applied allowing the adequate preprocessing over the dataset images. Then, clustering methods are used for obtaining the tumor morphology. The relative percentage error and the accuracy rate were the metrics considered to determine the performance of the proposed technique. The values obtained from the metrics used reflect an excellent correlation between the morphology of the tumor, generated manually by a pneumologist and the values obtained by the proposed technique. In the clinical and surgical contexts, the characterization of the detected lung tumor is made in terms of volume occupied by the tumor and it allows the monitoring of this disease as well as the activation of the respective protocols for its approach.