Mohan, J., Krishnaveni, V., Guo, Y.: A survey on the magnetic resonance image denoising methods. Biomed. Signal Process. Control 9 , 56–69 (2014)Mohan J, Krishnaveni V, Guo Y (2014) A survey on the magnetic re
Functional magnetic resonance imaging Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Abbreviations HR: Ground truth MR image SR: Super-resolved MR image ...
The use of GANs in medical imaging is well documented in a survey by Yi et al. [39]. This survey covers the use of GANs in reconstruction such as CT denoising [40], accelerated magnetic resonance imaging [41], PET denoising [42], and the application of super-resolution GANs in retinal...
Gómez-Guzmán MA, Jiménez-Beristaín L, García-Guerrero EE, López-Bonilla OR, Tamayo-Perez UJ, Esqueda-Elizondo JJ, Palomino-Vizcaino K, Inzunza-González E (2023) Classifying brain tumors on magnetic resonance imaging by using convolutional neural networks. Electronics 12(4):955 Google Scholar...
"SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning." ArXiv (2023). [paper] [2023.04] SAMCOD: Lv Tang, Haoke Xiao, Bo Li. "Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection." ArXiv (2023). [paper]...
(Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models,...
Segmentation datasets are created from unimodal or multimodal pictures obtained by professional medical equipment such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography (US). Traditional nondeep learning medical picture segmentation approaches depend mostly on thresholding [1...
1. Impulse and Gaussian denoising filters are further detailed in sections II and III, respectively. Section snippets Spatial Non-linear filters Spatial filters are obviously defined in the normal 2-D image space, where the intensity of each pixel is adjusted based on its original value and that...
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very
magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography (US). Traditional nondeep learning medical picture segmentation approaches depend mostly on thresholding [1], region growth [2], border detection [3], and other techniques. To produce superior segmentation results, ...