scholarly journals Germanene structure enhancement by adjacent insoluble domains of lead

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Ting-Yu Chen ◽  
David Mikolas ◽  
Santosh Chiniwar ◽  
Angus Huang ◽  
Chung-Huang Lin ◽  
...  
Keyword(s):  
Structure Enhancement

Does Chromoendoscopy with Structure Enhancement Improve the Colonoscopic Adenoma Detection Rate?

Endoscopy ◽  
2006 ◽  
Vol 38 (5) ◽  
pp. 444-448 ◽  
Author(s):  
M.-G. Lapalus ◽  
T. Helbert ◽  
B. Napoleon ◽  
J. Rey ◽  
P. Houcke ◽  
...  
Keyword(s):  
Detection Rate ◽  
Adenoma Detection Rate ◽  
Adenoma Detection ◽  
Structure Enhancement

scholarly journals Unsupervised Three-Dimensional Tubular Structure Segmentation via Filter Combination

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Hengfei Cui ◽  
Chang Yuwen ◽  
Lei Jiang
Keyword(s):  
Three Dimensional ◽  
Processing Technique ◽  
Medical Image Segmentation ◽  
Tubular Structure ◽  
Tubular Structures ◽  
Ranking Methods ◽  
Real Patient ◽  
The Public ◽  
Segmentation Methods ◽  
Structure Enhancement

AbstractTubular structure enhancement plays an utmost role in medical image segmentation as a pre-processing technique. In this work, an unsupervised 3D tubular structure segmentation technique is developed, which is mainly inspired by the idea of filter combination. Three well-known vessel filters, Frangi’s filter, the modified Frangi’s filter and the Multiscale Fractional Anisotropic Tensor (MFAT) filter, separately enhance the original images. Next, the enhanced images obtained using three different filters are combined. Different categories of vessel filters have the ability of complementarity, which is the main motivation of combining these three advanced filters. The combination of them ensures a high diversity of the enhancing results. Weighted mean and median ranking methods are used to conduct the operation of filter combination. Based on the optimized weights for all the three individual filters, fuzzy C-means method is then applied to segment the tubular structures. The proposed technique is tested on the public DRIVE and STARE datasets, the public synthetic vascular models (2011 and 2013 VascuSynth Sample), and real-patient Coronary Computed Tomography Angiography (CCTA) datasets. Experimental results demonstrate that the proposed technique outperforms the state-of-the-art filter combination-based segmentation methods. Moreover, our proposed method is able to yield better tubular structure segmentation results than that of each individual filter, which exhibits the superiority of the proposed method. In conclusion, the proposed method can be further used to facilitate vessel segmentation in medical practice.

Two stage residual CNN for texture denoising and structure enhancement on low dose CT image

2020 ◽  
Vol 184 ◽  
pp. 105115
Author(s):  
Liangliang Huang ◽  
Huiyan Jiang ◽  
Shaojie Li ◽  
Zhiqi Bai ◽  
Jitong Zhang
Keyword(s):  
Low Dose ◽  
Ct Image ◽  
Two Stage ◽  
Low Dose Ct ◽  
Structure Enhancement

Band structure enhancement of indirect transitions

1977 ◽  
Vol 21 (5) ◽  
pp. 437-439 ◽  
Author(s):  
D. Auvergne ◽  
P. Merle ◽  
H. Mathieu
Keyword(s):  
Band Structure ◽  
Structure Enhancement

High density liquid structure enhancement in glass forming aqueous solution of LiCl

2018 ◽  
Vol 148 (22) ◽  
pp. 222829 ◽  
Author(s):  
G. Camisasca ◽  
M. De Marzio ◽  
M. Rovere ◽  
P. Gallo
Keyword(s):  
Aqueous Solution ◽  
Liquid Structure ◽  
High Density ◽  
Glass Forming ◽  
Structure Enhancement

scholarly journals The relationship between curvilinear structure enhancement and ridge detection methods

2020 ◽  
Author(s):  
Haifa F. Alhasson ◽  
Chris G. Willcocks ◽  
Shuaa S. Alharbi ◽  
Adetayo Kasim ◽  
Boguslaw Obara
Keyword(s):  
Detection Methods ◽  
Ridge Detection ◽  
Structure Enhancement ◽  
The Relationship
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