Lung Segmentation in Chest Computerized Tomography Images Using the Border Following Algorithm

This paper proposes a new method of lung segmentation in chest Computerized Tomography (CT) images called Follower of Lung Contour (FLC). This method works as follows: firstly, the image pixels are classified as pulmonary or not through an Artificial Neural Network (ANN) Multilayer Perceptron (MLP) based on pulmonary radiologic densities. After this, the lung detection is made based on achieved through the Border Following Algorithm together with predetermined rules that consider the detected objects area and positioning on the image. The proposed method validation is performed considering as Gold Standard a manual segmentation realized by a pulmonologist at Walter Cantidio Hospital of Federal University of Ceara. Moreover, 30 chest CT images were used, in which 10 are from patients diagnosed with Fibrosis, 10 are from patients with Chronic Obstructive Pulmonary Disease (COPD) and 10 are from healthy patients. The FLC results are compared with six other segmentation methods results using the Gold Standard as reference. Thus, the FLC algorithm shows good results with an average accuracy of 98% and average harmonic means of 98%. Furthermore, it can be concluded that this method may be part of a system to aid in medical diagnosis on Pulmonology.