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

2017/05/11 by Dufan Wu, Wu, Dufan, Kyung Sang Kim +6 · 2 citations
Computer Science · Engineering · Mathematics · Medicine · #Advanced X-ray and CT Imaging #Image and Signal Denoising Methods #Medical Imaging Techniques and Applications #cs.CV #stat.ML

paper · pdf · doi:10.48550/arxiv.1705.04267

9 pages, 9 figures

arxiv created 2017/08/28 · arxiv updated 2017/08/29

Abstract

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.

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• Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
• Generative Tomography Reconstruction

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