INTRODUCTION: Among the diseases that affect the world's population, there is concern about Chronic Obstructive Pulmonary Disease (COPD), that, according to the World Health Organization, could be the leading cause of death worldwide by the year 2030. Aiming to contribute to aid medical diagnosis, this research focuses its efforts on the segmentation of the lungs, since this is the basic step system in the area of Computer Vision pulmonology. METHODS: This paper proposes a new method for segmentation of lung images in Computed Tomography (CT) of the chest called Active Contour Method (MCA) Crisp Adaptive 2D. This MCA is to draw a curve starting inside an object of interest. This curve is deformed by successive iterations, minimizing energies that act on it, moving it to the edges of the object. The MCA is the improvement of the proposed MCA Crisp previously developed, aiming to increase the accuracy, decreasing analysis time and reducing the subjectivity in the segmentation and analysis of the lungs of these images by pulmonologists. This method is automatically initialized in the lungs and on successive iterations to minimize this energy, this MCA automatically targets the lungs in chest CT images. RESULTS: To evaluate the proposed method, the MCA Adaptive Crisp is compared with MCAs THRMulti, THRMod, GVF, VFC, Crisp and also with the system SISDEP, this assessment is performed using reference images 24, 12 COPD patients and 12 volunteers healthy, manually segmented by a pulmonologist. The results show that the proposed method is superior to the others. CONCLUSION: Based on the results, it can be concluded that this method can integrate systems aid in the medical diagnosis of Pulmonology.
Aid diagnostic; Active Contour Models; Lung segmentation; Chest CT images
