Stochastic resonance in a time-delayed tumor cell growth system driven by additive and multiplicative noises

Zhang, Gang; Shi, Jiabei; Zhang, Tianqi

doi:10.1142/s0217984918502597

Cited by 7 publications

(1 citation statement)

References 37 publications

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“…The speckle noise information is complex, including multiplicative and additive noise [22]. Formula ( 4) is constructed to represent speckle noise, where f (x, y) is a CT image with speckle noise, g(x, y) is a real image without speckle noise, n 1 (x, y) is a multiplicative noise part, and n 2 (x, y) is an additive noise part.…”

Section: ) Speckle Noise Of Ct Imagesmentioning

confidence: 99%

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

et al. 2021

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Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses. The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases. CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis. In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex. To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing. First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm. The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges. Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction. A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed. With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency. The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy. The experimental results show that MF-GAN has a better denoising effect than other algorithms. Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.INDEX TERMS Internet of Things, edge computing, generative adversarial network, 3D reconstruction.

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Section: ) Speckle Noise Of Ct Imagesmentioning

confidence: 99%