Journal ArticleDOI
Ultrasound Image Enhancement Using Structure Oriented Adversarial Network
TLDR
Experimental evaluations show that the proposed DRNN outperforms the state-of-the-art despeckling approaches in terms of the structural similarity index measure, peak signal to noise ratio, edge preservation index, and speckle region's signal-to- noise ratio.Abstract:
In this letter, we aim to develop a deep adversarial despeckling approach to enhance the quality of ultrasound images. Most of the existing approaches target a complete removal of speckle, which produces oversmooth outputs and results in loss of structural details. In contrast, the proposed approach reduces the speckle extent without altering the structural and qualitative attributes of the ultrasound images. A despeckling residual neural network (DRNN) is trained with an adversarial loss imposed by a discriminator. The discriminator tries to differentiate between the despeckled images generated by the DRNN and the set of high-quality images. Further to prevent the developed network from oversmoothing, a structural loss term is used along with the adversarial loss. Experimental evaluations show that the proposed DRNN outperforms the state-of-the-art despeckling approaches in terms of the structural similarity index measure, peak signal to noise ratio, edge preservation index, and speckle region's signal to noise ratio.read more
Citations
More filters
Journal ArticleDOI
Deep Learning in the Biomedical Applications: Recent and Future Status
TL;DR: This paper reviews the major deep learning concepts pertinent to biomedical applications and concludes with a critical discussion, interpretation and relevant open challenges of the Omics and the BBMI.
Journal ArticleDOI
DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images.
TL;DR: The proposed deep convolutional neural network for speckle reduction in retinal OCT images, termed DeSpecNet, outperforms state-of-the-art methods in suppressing speckles and revealing subtle features while preserving edges.
Journal ArticleDOI
Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical applications: an Overview
Carlos Andrés Duarte-Salazar,Andrés Eduardo Castro-Ospina,Miguel A. Becerra,Edilson Delgado-Trejos +3 more
TL;DR: This paper describes, in detail, 27 techniques that mainly focus on the smoothing or elimination of speckle noise in medical ultrasound images, and describes recent techniques in the field of machine learning focused on deep learning, which are not yet well known but greatly relevant.
Journal ArticleDOI
Carotid artery ultrasound image analysis: A review of the literature:
TL;DR: The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases.
Proceedings ArticleDOI
Ultrasound Speckle Reduction Using Generative Adversial Networks
TL;DR: Results show that GANs can learn ultrasound speckle reduction, and even though the training set consisted only of cardiac ultrasound images, results from other parts of the body and scanners indicate that the method learns speckel reduction in general, and not just for cardiac images.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Posted Content
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
Related Papers (5)
Real-Time Ultrasound Image Despeckling Using Mixed-Attention Mechanism Based Residual UNet
Yancheng Lan,Xuming Zhang +1 more