Computer-aided Detection For Pneumoconiosis On Digital Chest Radiography

Pneumoconiosis is a serious occupational disease on the world wide with high incidence. The computer aided detection for pneumoconiosis is meaningful to high volume screening, which helps doctors optimize the diagnosis workflow, reduce workload and improve detection sensitivity. This paper presents an automatic computer aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The classification performances using the full feature vectors are the sensitivity 92.0%, the specificity 87.7%, the accuracy 88.9% and the area under the curve of the received operating characteristic is 0.978. While the classification performances using the selected feature vectors are the sensitivity 91.2%, the specificity 86.3%, the accuracy 87.8% and the area under the curve of the received operating characteristic is 0.958. Compared to the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic.