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A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

By Dufan Wu, Kyungsang Kim, Georges El Fakhri and Quanzheng Li


Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in the denoised image due to complexity of noises. A cascaded training network was proposed in this work, where the trained CNN was applied on the training dataset to initiate new trainings and remove artifacts induced by denoising. A cascades of convolutional neural networks (CNN) were built iteratively to achieve better performance with simple CNN structures. Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to evaluate the method's performance.Comment: 9 pages, 9 figure

Topics: Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, I.2.6, I.4.3, J.3
Year: 2017
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