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

Domaine:

healthcare

Type de record:

paperdataset
Créateur:
Wu,KimFakLi,
Hôte:avatar
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. 9 pages, 9 figures

Visit

arxiv.org

Tasks

computer vision

Tags

Computer Vision and Pattern RecognitionMachine LearningI.2.6; I.4.3; J.3

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