AI Medical Compendium Topic:
Signal-To-Noise Ratio

Clear Filters Showing 641 to 650 of 824 articles

Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles could lead to dysphagia. However, there is a lack of standardized and non-invasive methods that suppo...

Developing Noise-Resistant Three-Dimensional Single Particle Tracking Using Deep Neural Networks.

Analytical chemistry
Three-dimensional single particle tracking (3D SPT) is a powerful tool in various chemical and biological studies. In 3D SPT, z sensitive point spread functions (PSFs) are frequently used to generate different patterns, from which the axial position ...

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant radiation doses, resulting from high image quality requirements and acquisitions at multiple cardiac ph...

Computed tomography super-resolution using deep convolutional neural network.

Physics in medicine and biology
The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolutio...

Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...

Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.

Japanese journal of radiology
PURPOSE: To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.

Threshold-Based Noise Detection and Reduction for Automatic Speech Recognition System in Human-Robot Interactions.

Sensors (Basel, Switzerland)
This work develops a speech recognition system that uses two procedures of proposed noise detection and combined noise reduction. The system can be used in applications that require interactive robots to recognize the contents of speech that includes...

A support vector machine approach for AF classification from a short single-lead ECG recording.

Physiological measurement
OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum features, and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF),...

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.

Computers in biology and medicine
In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which ca...

Robust 3D point cloud registration based on bidirectional Maximum Correntropy Criterion.

PloS one
This paper presents a robust 3D point cloud registration algorithm based on bidirectional Maximum Correntropy Criterion (MCC). Comparing with traditional registration algorithm based on the mean square error (MSE), using the MCC is superior in dealin...