Usefulness of digital images segmentation in pulmonary transplantation

Abstract

Abstract In the presence of pulmonary pathologies such as chronic obstructive pulmonary disease, diffuse pulmonary disease and cystic fibrosis, among others, it is common to require the removal or replacement of a portion of lungs. There are several requirements for both donors and organ receivers (recipients) established in the literature. May be the main one is the volume that the donor’s lungs occupy in the thoracic cavity. This parameter is vital because if the volume of the lungs exceeds the thoracic cavity of the recipients the transplant, logically, is unfeasible for physical reasons such as the incompatibility between the receiver lung volume and the donor lung volume. In this sense, the present paper proposes the creation of a hybrid technique, based on digital image processing techniques application to raise the quality of the information related to lungs captured in three-dimensional sequences of computed tomography and for generating the morphology and the volumes of the lungs, belonging to a patient. During the filtering stage median, saturated and gradient magnitude filters are applied with the purpose of addressing the noise and artefacts images problems; whereas during the segmentation stage, methods based on clustering processes are used to extract the lungs from the images. The values obtained for the metric that assesses the quality of the hybrid computational technique reflect its good performance. Additionally, these results are very important in clinical processes where both the shapes and volumes of lungs are vital for monitoring some lung diseases that can affect the normal lung physiology.