title: Three cuts method for identification of COPD.
abstract: Two main forms of COPD (Chronic Obstructive Pulmonary Disease) refer to a group of lung diseases that block airflow and cause a huge degree of human suffering. A new method for identifying and estimating the severity of COPD from three-dimensional (3-D) pulmonary X-ray CT images would be helpful for evaluation of treatment effects and early diagnosing is presented in this paper. This method has five main steps. Firstly, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Secondly, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations of three main cuts which selected by a feed-forward neural network are found based on the inspiratory and expiratory states. Finally, a pattern classifier is used to decide about the disease and its severity. Twenty patients with air-trapping problems and twelve normal adults were enrolled in this study. Based on the results, a mathematical model was developed to relate variations of lung volumes to severity of disease. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the accuracy of our method for right regions were %81.6, %80.5, %87.5, %72.5 and %81.3 respectively. And these parameters for left regions were %90, %83.3, %90, %83.3 and %87.5 respectively. The proposed method may assist radiologists in detection of Asthma and COPD as a computer aided diagnosis (CAD) system.
authors.affiliation: Department of Electrical and Computer Engineering, Wayne State University, AND Medical Image Analysis Laboratory, Radiology and Research Administration Departments, Henry Ford Health System, Detroit, MI, USA. parsa.hosseini@wayne.edu.
authors.forename: Mohammad Parsa
authors.initials: MP
authors.lastname: Hosseini
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.forename: Shahram
authors.initials: S
authors.lastname: Akhlaghpoor
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis* / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed