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 study 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 recognise 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: Zhu L, Zheng R, Jin H, Zhang Q, Zhang W. ;Full Source: Bio-Medical Materials and Engineering. 2014 Jan 1;24(6):3389-95. doi: 10.3233/BME-141162. ;