Computer-assisted detection of infectious lung diseases: A review

被引：64

作者：

Bagci, Ulas ^{[1
]}

Bray, Mike ^{[2
]}

Caban, Jesus ^{[3
]}

Yao, Jianhua ^{[4
]}

Mollura, Daniel J. ^{[1
]}

机构：

[1] NIH, Ctr Infect Dis Imaging, Dept Radiol & Imaging Sci, Bethesda, MD 20892 USA

[2] NIAID, NIH, Bethesda, MD 20892 USA

[3] NIH, Natl Lib Med, Bethesda, MD 20892 USA

[4] NIH, Dept Radiol & Imaging Sci, Bethesda, MD 20892 USA

来源：

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2012年 / 36卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Infectious diseases; Computer assisted detection; Texture analysis; Lung CT; Feature extraction; Tomography; HIGH-RESOLUTION CT; ARTIFICIAL NEURAL-NETWORK; IMAGE FEATURE ANALYSIS; GROUND-GLASS OPACITY; AIDED DIAGNOSIS; CHEST RADIOGRAPHS; TEXTURE CLASSIFICATION; INTERSTITIAL DISEASE; DIGITAL RADIOGRAPHY; PULMONARY NODULES;

D O I：

10.1016/j.compmedimag.2011.06.002

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges. Published by Elsevier Ltd.

引用

页码：72 / 84

页数：13

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