title: Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans.
abstract: This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) Möbius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Interobserver and observer-computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%. Moreover, correlations of observer-observer   (R(2)=0.8848,   and observer-CAD agreements  (R(2)=0.824,   validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns.
authors.affiliation: Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892, USA. ulasbagci@ieee.org
authors.forename: Ulas
authors.initials: U
authors.lastname: Bagci
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.forename: Albert
authors.initials: A
authors.lastname: Wu
authors.forename: Jesus
authors.initials: J
authors.lastname: Caban
authors.forename: Tara N
authors.initials: TN
authors.lastname: Palmore
authors.forename: Anthony F
authors.initials: AF
authors.lastname: Suffredini
authors.forename: Omer
authors.initials: O
authors.lastname: Aras
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Mollura
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*