Computer-assisted detection of infectious lung diseases: a review - PubMed (original) (raw)
Review
Computer-assisted detection of infectious lung diseases: a review
Ulaş Bağcı et al. Comput Med Imaging Graph. 2012 Jan.
Abstract
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.
Figures
Figure 1
Anatomical lung segments: B1: Apical, B2: Posterior, B3: Anterior, B4: Lateral, B5: Medial, B6: Superior, B7: Basal Medial, B8: Basal Anterior, B9: Basal Lateral, B10: Basal Posterior (Partly from [119], with permission).
Figure 2
Overview of different feature sets used in CAD systems for lung diseases.
Figure 3
a. Consolidation, b. nodules and nodular structures, c. ground glass nodular opacities.
Figure 4
(a) Reticular, (b) GGO, (c) tree-in-bud patterns.
Figure 5
An example of a CAD system using CT scans. Texture analysis is based on manual labelling of textures or shapes.
Figure 6
The most commonly used classifiers for CAD systems, with their organizational principles.
Figure 7
Tree-in-bud detection with PET imaging (Left: CT, Middle: PET, Right: superimposed PET-CT, Rightmost: zoomed)
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