Bağcı Ulaş, Yao Jianhua, Caban Jesus, Palmore Tara N, Suffredini Anthony F, Mollura Daniel J
Department of Radiology andImaging Sciences, National Institutes of Health, Center for Infectious Diseases Imaging, Bethesda, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5096-9. doi: 10.1109/IEMBS.2011.6091262.
Abnormal nodular branching opacities at the lung periphery in Chest Computed Tomography (CT) are termed by radiology literature as tree-in-bud (TIB) opacities. These subtle opacity differences represent pulmonary disease in the small airways such as infectious or inflammatory bronchiolitis. Precisely quantifying the detection and measurement of TIB abnormality using computer assisted detection (CAD) would assist clinical and research investigation of this pathology commonly seen in pulmonary infections. This paper presents a novel method for automatically detecting TIB patterns based on fast localization of candidates using local scale information of the images. The proposed method combines shape index, local gradient statistics, and steerable wavelet features to automatically identify TIB patterns. Experimental results using 39 viral bronchiolitis human para-influenza (HPIV) CTs and 21 normal lung CTs achieved an overall accuracy of 89.95%.
胸部计算机断层扫描（CT）显示的肺周边异常结节状分支状混浊在放射学文献中被称为芽生树（TIB）混浊。这些细微的混浊差异代表了小气道中的肺部疾病，如感染性或炎症性细支气管炎。使用计算机辅助检测（CAD）精确量化TIB异常的检测和测量，将有助于对肺部感染中常见的这种病理情况进行临床和研究调查。本文提出了一种基于利用图像局部尺度信息快速定位候选区域来自动检测TIB模式的新方法。所提出的方法结合了形状指数、局部梯度统计和可操纵小波特征来自动识别TIB模式。使用39例人类副流感病毒（HPIV）病毒性细支气管炎CT和21例正常肺CT的实验结果，总体准确率达到了89.95%。
