Approximate shading for the re-illumination of synthetic images

2005 ◽  
Author(s):  
R. Scoggins ◽  
R. Machiraju ◽  
R.J. Moorhead
Keyword(s):  
Synthetic Images

EVALUATION OF THE RAINBOW VOLUMIC VELOCIMETRY (RVV) PROCESS BY SYNTHETIC IMAGES

2007 ◽  
Vol 14 (1) ◽  
pp. 1-15 ◽  
Author(s):  
R. Malfara ◽  
Y. Bailly ◽  
J. P. Prenel ◽  
C. Cudel
Keyword(s):  
Synthetic Images

scholarly journals A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images

2021 ◽  
Vol 202 ◽  
pp. 105958
Author(s):  
Antón Cid-Mejías ◽  
Raúl Alonso-Calvo ◽  
Helena Gavilán ◽  
José Crespo ◽  
Víctor Maojo
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Synthetic Images

scholarly journals Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ji Eun Park ◽  
Dain Eun ◽  
Ho Sung Kim ◽  
Da Hyun Lee ◽  
Ryoung Woo Jang ◽  
...  
Keyword(s):  
Isocitrate Dehydrogenase ◽  
Synthetic Data ◽  
Diagnostic Model ◽  
Generative Adversarial Network ◽  
Classification Rate ◽  
Mutant Type ◽  
Clinical Model ◽  
The Real ◽  
Adversarial Network ◽  
Synthetic Images

AbstractGenerative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model.

Forgery detection using multiple light sources for synthetic images

2017 ◽  
Vol 51 (3) ◽  
pp. 243-250 ◽  
Author(s):  
Manoj Kumar ◽  
Sangeet Srivastava ◽  
Nafees Uddin
Keyword(s):  
Light Sources ◽  
Forgery Detection ◽  
Synthetic Images

Optimization of T2-weighted imaging for shoulder magnetic resonance arthrography by synthetic magnetic resonance imaging

2017 ◽  
Vol 59 (8) ◽  
pp. 959-965
Author(s):  
Seung Hyun Lee ◽  
Young Han Lee ◽  
Seok Hahn ◽  
Jaemoon Yang ◽  
Ho-Taek Song ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Supraspinatus Tendon ◽  
Joint Fluid ◽  
Resonance Imaging ◽  
Mr Images ◽  
Diagnostic Agreement ◽  
Tissue Contrast ◽  
Synthetic Images ◽  
Synthetic Mri

Background Synthetic magnetic resonance imaging (MRI) allows reformatting of various synthetic images by adjustment of scanning parameters such as repetition time (TR) and echo time (TE). Optimized MR images can be reformatted from T1, T2, and proton density (PD) values to achieve maximum tissue contrast between joint fluid and adjacent soft tissue. Purpose To demonstrate the method for optimization of TR and TE by synthetic MRI and to validate the optimized images by comparison with conventional shoulder MR arthrography (MRA) images. Material and Methods Thirty-seven shoulder MRA images acquired by synthetic MRI were retrospectively evaluated for PD, T1, and T2 values at the joint fluid and glenoid labrum. Differences in signal intensity between the fluid and labrum were observed between TR of 500–6000 ms and TE of 80–300 ms in T2-weighted (T2W) images. Conventional T2W and synthetic images were analyzed for diagnostic agreement of supraspinatus tendon abnormalities (kappa statistics) and image quality scores (one-way analysis of variance with post-hoc analysis). Results Optimized mean values of TR and TE were 2724.7 ± 1634.7 and 80.1 ± 0.4, respectively. Diagnostic agreement for supraspinatus tendon abnormalities between conventional and synthetic MR images was excellent (κ = 0.882). The mean image quality score of the joint space in optimized synthetic images was significantly higher compared with those in conventional and synthetic images (2.861 ± 0.351 vs. 2.556 ± 0.607 vs. 2.750 ± 0.439; P < 0.05). Conclusion Synthetic MRI with optimized TR and TE for shoulder MRA enables optimization of soft-tissue contrast.

Comparison of synthetic images generated from LiDAR intensity and passive hyperspectral imagery

2014 ◽  
Author(s):  
Preston J. Hartzell ◽  
Juan Carlos Fernandez-Diaz ◽  
Xiao Wang ◽  
Craig L. Glennie ◽  
William E. Carter ◽  
...  
Keyword(s):  
Hyperspectral Imagery ◽  
Synthetic Images
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