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An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs

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Abstract

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 results show that the classification performances are high. Compared with 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.

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Acknowledgements

This work is supported in part by the Science and Technology Commission of Shanghai Municipality, China (Grant No.074107022), the National High Technology Research and Development Program of China (863 Program) (2007AA02Z452), National Natural Science Foundation of China (30570511 and 30770589) and National Basic Research Program of China (2010CB834300).

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, NO.800, Dongchuan Road, Shanghai, 200240, China

    Peichun Yu, Ying Zhu & Jun Zhao

  2. Imaging Technologies Lab, GE Global Research, Shanghai, 201203, China

    Hao Xu & Chao Yang

  3. Shanghai Pulmonary Hospital, Shanghai, 200433, China

    Xiwen Sun

Authors
  1. Peichun Yu
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  2. Hao Xu
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  3. Ying Zhu
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  4. Chao Yang
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  5. Xiwen Sun
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  6. Jun Zhao
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Correspondence to Jun Zhao.

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Yu, P., Xu, H., Zhu, Y. et al. An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs. J Digit Imaging 24, 382–393 (2011). https://doi.org/10.1007/s10278-010-9276-7

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  • DOI: https://doi.org/10.1007/s10278-010-9276-7

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