Real time ultrasound image denoising using NVIDIA CUDA

Fredj, Amira Hadj; Malek, Jihene

doi:10.1109/atsip.2016.7523083

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…The use of a committed filter to reconfigure the anisotropic distribution filtration system to convey the attributes of exponential distribution in ultrasound frames is indeed the basis of the video restoration method Fredj et al [38] propose. The authors created the system to utilize parallel processors to speed up the diffusion function execution by optimizing graphics on a quad-core unit.…”

Section: Transform Domain Filtersmentioning

confidence: 99%

Investigation on ultrasound images for detection of fetal congenital heart defects

Rufus

2024

Biomed. Phys. Eng. Express

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Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

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Section: Transform Domain Filtersmentioning

confidence: 99%

Investigation on ultrasound images for detection of fetal congenital heart defects

Rufus

2024

Biomed. Phys. Eng. Express

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U-Net-Based Denoising Autoencoder Network for De-Speckling in Fetal Ultrasound Images

Satish,

Herald Anantha Rufus,

Antony Freeda Rani

et al. 2023

Lecture Notes in Networks and Systems

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Real-time denoising of ultrasound images based on deep learning

Cammarasana

Nicolardi

Patanè

2022

Med Biol Eng Comput

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Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g. segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g. spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.

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Real time ultrasound image denoising using NVIDIA CUDA

Cited by 7 publications

References 9 publications

Investigation on ultrasound images for detection of fetal congenital heart defects

Investigation on ultrasound images for detection of fetal congenital heart defects

U-Net-Based Denoising Autoencoder Network for De-Speckling in Fetal Ultrasound Images

Real-time denoising of ultrasound images based on deep learning

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