Robust Needle Localization and Enhancement Algorithm for Ultrasound by Deep Learning and Beam Steering Methods

2021 ◽  
Vol 36 (2) ◽  
pp. 334-346
Author(s):  
Jun Gao ◽  
Paul Liu ◽  
Guang-Di Liu ◽  
Le Zhang
Keyword(s):  
Deep Learning ◽  
Beam Steering ◽  
Needle Localization ◽  
Enhancement Algorithm ◽  
Steering Methods

scholarly journals Review of lens-assisted beam steering methods

2022 ◽  
Vol 2 (01) ◽  
Author(s):  
Steven J. Spector
Keyword(s):  
Beam Steering ◽  
Steering Methods

scholarly journals An image enhancement algorithm of video surveillance scene based on deep learning

2021 ◽  
Author(s):  
Wei‐wei Shen ◽  
Lin Chen ◽  
Shuai Liu ◽  
Yu‐Dong Zhang
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Video Surveillance ◽  
Enhancement Algorithm

Typical Array Geometries and Basic Beam Steering Methods

2012 ◽  
pp. 23-54 ◽  
Author(s):  
Victor Rabinovich ◽  
Nikolai Alexandrov
Keyword(s):  
Beam Steering ◽  
Steering Methods

Needle Localization for Robot-assisted Subretinal Injection based on Deep Learning

2019 ◽  
Author(s):  
Mingchuan Zhou ◽  
Xijia Wang ◽  
Jakob Weiss ◽  
Abouzar Eslami ◽  
Kai Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Robot Assisted ◽  
Needle Localization ◽  
Subretinal Injection

Enhanced needle localization in ultrasound using beam steering and learning-based segmentation

2015 ◽  
Vol 41 ◽  
pp. 46-54 ◽  
Author(s):  
Charles R. Hatt ◽  
Gary Ng ◽  
Vijay Parthasarathy
Keyword(s):  
Beam Steering ◽  
Needle Localization

scholarly journals Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

2020 ◽  
Vol 9 ◽  
Author(s):  
Yuma Kinoshita ◽  
Hitoshi Kiya
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Color Image ◽  
Mapping Method ◽  
Experimental Results ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Color Image Enhancement ◽  
Correction Scheme ◽  
Enhancement Algorithm

In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.

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