A review of state-of-the-art speckle reduction techniques for optical coherence tomography fingertip scans

2015 ◽  
Author(s):  
Luke N. Darlow ◽  
Sharat S. Akhoury ◽  
James Connan
Keyword(s):  
Optical Coherence Tomography ◽  
State Of The Art ◽  
Speckle Reduction ◽  
Optical Coherence ◽  
Reduction Techniques

Speckle reduction in optical coherence tomography by frequency compounding

2003 ◽  
Vol 8 (3) ◽  
pp. 565 ◽  
Author(s):  
Michael Pircher ◽  
Erich Götzinger ◽  
Rainer Leitgeb ◽  
Adolf F. Fercher ◽  
Christoph. K. Hitzenberger
Keyword(s):  
Optical Coherence Tomography ◽  
Speckle Reduction ◽  
Optical Coherence

Speckle reduction in optical coherence tomography by two-step image registration

2015 ◽  
Vol 20 (3) ◽  
pp. 036013 ◽  
Author(s):  
Hang Zhang ◽  
Zhongliang Li ◽  
Xiangzhao Wang ◽  
Xiangyang Zhang
Keyword(s):  
Optical Coherence Tomography ◽  
Image Registration ◽  
Speckle Reduction ◽  
Optical Coherence

Speckle Reduction in Optical Coherence Tomography

2008 ◽  
Vol 35 (9) ◽  
pp. 1437-1440 ◽  
Author(s):  
沈婷梅 Shen Tingmei ◽  
顾瑛 Gu Ying ◽  
王天时 Wang Tianshi ◽  
马国江 Ma Guojiang
Keyword(s):  
Optical Coherence Tomography ◽  
Speckle Reduction ◽  
Optical Coherence

scholarly journals MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 60 ◽  
Author(s):  
Wen Liu ◽  
Yankui Sun ◽  
Qingge Ji
Keyword(s):  
Optical Coherence Tomography ◽  
State Of The Art ◽  
Imaging Technique ◽  
Optical Coherence ◽  
Convolutional Network ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Benchmark Datasets ◽  
Resolution Imaging ◽  
Layer Segmentation

Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.

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