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Proceedings Paper

Combined global and local information for blind CT image quality assessment via deep learning
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Paper Abstract

Image quality assessment (IQA) is an important step to determine whether the computed tomography (CT) images are suitable for diagnosis. Since the high dose CT images are usually not accessible in clinical practice, no-reference (NR) CT IQA should be used. Most NR-IQA methods for CT images based on deep learning strategy focus on global information and ignores local performance, i.e., contrast, edge of local region. In this work, to address this issue, we presented a new NR-IQA framework combining global and local information for CT images. For simplicity, the NR-IQA framework is termed as NR-GL-IQA. In particular, the presented NR- GL-IQA adopts a convolutional neural network to predict entire image quality blindly without a reference image. In this stage, an elaborate strategy is used to automatically label the entire image quality for neural network training to cope with the problem of time-consuming in manually massive CT images annotation. Second, in the presented NR-GL-IQA method, Perception-based Image QUality Evaluator (PIQUE) is used to predict the local region quality because the PIQUE can adaptively capture the local region characteristics. Finally, the overall image quality is estimated by combining the global and local IQA together. The experimental results with Mayo dataset demonstrate that the presented NR-GL-IQA method can accurately predicts CT image quality and the combination of global and local IQA is closer to the radiologist assessment than that with only one single assessment.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131615 (16 March 2020); doi: 10.1117/12.2548953
Show Author Affiliations
Qi Gao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Manman Zhu, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Danyang Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Qingwen Lv, Zhujiang Hospital, Southern Medical Univ. (China)
Dong Zeng, South China Univ. of Technology (China)
Jianhua Ma Sr., Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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