Analysis of chronic obstructive pulmonary disease (COPD) using CT images

• Main Author: Bodduluri, Sandeep
• Other Authors: Reinhardt, Joseph M.
• Format: Others
• Language: English
• Published: University of Iowa 2012
• Subjects: classification; CT COPD; density texture; images; jacobian; Biomedical Engineering and Bioengineering
• Online Access: https://ir.uiowa.edu/etd/2441; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=4569&context=etd

Chronic Obstructive Pulmonary Disease (COPD), a growing health concern, is the fourth leading cause of death in the United States. While people habituated to smoking constitute the highest COPD susceptible population, people exposed to air pollution or other lung irritants also form a major group of potential COPD patients. COPD is a progressive disease that is characterized by the combination of chronic bronchitis, small airway obstruction, and emphysema that causes an overall decrease in the lung elasticity affecting the lung tissue. The current gold standard method to diagnose COPD is by pulmonary function tests (PFT) which measures the extent of COPD based on the lung volumes and is further classified into five severity stages. PFT measurements are insensitive to early stages of COPD and also its lack of reproducibility makes it hard to rely on, in assessing the disease progression. Alternatively, Pulmonary CT scans are considered as a major diagnostic tool in analyzing the COPD and CT measures are also closely related to the pathological extent of the disease. Quantification of COPD using features derived from CT images has been proven effective. The most common features are density based and texture based. We propose a new set of features called lung biomechanical features which capture the regional lung tissue deformation patterns during the respiratory cycle. We have tested these features on 75 COPD subjects and 15 normal subjects. We have done classification of COPD/Non COPD on the dataset using the three feature sets and also performed the classification all these subjects to their corresponding severity stage. It is shown that the lung biomechanical features were also able to classify COPD subjects with a good AUC. It is also shown that, by combining the best features from each feature set, there is an improvement in the classifier performance. Multiple regression analysis is performed to find the correlation between the CT derived features and PFT measurements.