title: Automatic detection and recognition of silicosis in chest radiograph.
abstract: Silicosis remains one of the most harmful occupational respiratory diseases. It threatens the workers exposed to dust environment. Chest radiograph is the main available image source for silicosis diagnosis according to the diagnostic criteria of pneumoconiosis (DCP). Automatic detection and recognition of silicosis in chest radiograph has great importance on aiding the process of silicosis diagnosis. This paper proposes a multi-scale opacity detection approach to detect all suspected opacities from the chest radiograph. A support vector machine (SVM) based computer-aided silicosis diagnosis is proposed to recognize silicosis opacity from a large amount of candidate regions, and gives processing result for radiologist reference. Comprehensive experiments conducted on real world chest radiographs demonstrate that the proposed approach can reveal changes of silicosis pathology well, and it can be adopted as an effective tool for automatic silicosis diagnosis.
authors.affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhu
authors.affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Ran
authors.initials: R
authors.lastname: Zheng
authors.affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Hai
authors.initials: H
authors.lastname: Jin
authors.affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012829:Silicosis / Q000000981:diagnostic imaging*; D060388:Support Vector Machine
keywords: Silicosis; chest radiograph; diagnostic criteria of pneumoconiosis; silicosis pathology; support vector machine