Hosseini Mohammad Parsa, Soltanian-Zadeh Hamid, Akhlaghpoor Shahram
Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Iran J Radiol. 2012 Mar;9(1):22-7. doi: 10.5812/iranjradiol.6759. Epub 2012 Mar 25.
Chronic obstructive pulmonary disease (COPD) is a devastating disease.While there is no cure for COPD and the lung damage associated with this disease cannot be reversed, it is still very important to diagnose it as early as possible.
In this paper, we propose a novel method based on the measurement of air trapping in the lungs from CT images to detect COPD and to evaluate its severity.
Twenty-five patients and twelve normal adults were included in this study. The proposed method found volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration were compared and changes in the areas and volumes of the lungs between inspiration and expiration were used to define quantitative measures (features). Using these features,the subjects were classified into two groups of normal and COPD patients using a Bayesian classifier. In addition, t-tests were applied to evaluate discrimination powers of the features for this classification.
For the cases studied, the proposed method estimated air trapping in the lungs from CT images without human intervention. Based on the results, a mathematical model was developed to relate variations of lung volumes to the severity of the disease.
As a computer aided diagnosis (CAD) system, the proposed method may assist radiologists in the detection of COPD. It quantifies air trapping in the lungs and thus may assist them with the scoring of the disease by quantifying the severity of the disease.
慢性阻塞性肺疾病（COPD）是一种具有破坏性的疾病。虽然COPD无法治愈，且与该疾病相关的肺损伤不可逆转，但尽早诊断仍然非常重要。
在本文中，我们提出一种基于从CT图像测量肺内气体潴留来检测COPD并评估其严重程度的新方法。
本研究纳入了25例患者和12名正常成年人。所提出的方法发现了从吸气到呼气过程中肺的容积变化。为此，比较了全吸气和全呼气时的气管CT图像，并利用吸气和呼气之间肺面积和容积的变化来定义定量指标（特征）。使用这些特征，通过贝叶斯分类器将受试者分为正常组和COPD患者组。此外，应用t检验来评估这些特征对于该分类的鉴别能力。
对于所研究的病例，所提出的方法无需人工干预即可从CT图像估计肺内的气体潴留。基于这些结果，建立了一个数学模型，将肺容积变化与疾病严重程度相关联。
作为一种计算机辅助诊断（CAD）系统，所提出的方法可能有助于放射科医生检测COPD。它对肺内气体潴留进行量化，从而可能通过量化疾病严重程度来协助他们对疾病进行评分。
