Suitable Image Intensity Normalization for Arterial Visualization

Off-axis electron holography has the well known ability to preserve the complex image wave within the final, recorded image. This final image described by I(x,y) = I(r) contains contributions from the image intensity of the elastically scattered electrons IeI (r) = |A(r) exp (iΦ(r)) |, the contributions from the inelastically scattered electrons IineI (r), and the complex image wave Ψ = A(r) exp(iΦ(r)) as:(1) I(r) = IeI (r) + Iinel (r) + μ A(r) cos(2π Δk r + Φ(r))where the constant μ describes the contrast of the interference fringes which are related to the spatial coherence of the electron beam, and Φk is the resulting vector of the difference of the wavefront vectors of the two overlaping beams. Using a software package like HoloWorks, the complex image wave Ψ can be extracted.

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Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI

Diagnostics ◽

10.3390/diagnostics11050816 ◽

2021 ◽

Vol 11 (5) ◽

pp. 816

Author(s):

Kuei-Yuan Hou ◽

Hao-Yuan Lu ◽

Ching-Ching Yang

Keyword(s):

Mean Value ◽

Tissue Type ◽

Routine Practice ◽

Intensity Level ◽

Mean Values ◽

Conversion Model ◽

Ratio Image ◽

Abdominal Mri ◽

High Level ◽

Intensity Normalization

This study aimed to facilitate pseudo-CT synthesis from MRI by normalizing MRI intensity of the same tissue type to a similar intensity level. MRI intensity normalization was conducted through dividing MRI by a shading map, which is a smoothed ratio image between MRI and a three-intensity mask. Regarding pseudo-CT synthesis from MRI, a conversion model based on a three-layer convolutional neural network was trained and validated. Before MRI intensity normalization, the mean value ± standard deviation of fat tissue in 0.35 T chest MRI was 297 ± 73 (coefficient of variation (CV) = 24.58%), which was 533 ± 91 (CV = 17.07%) in 1.5 T abdominal MRI. The corresponding results were 149 ± 32 (CV = 21.48%) and 148 ± 28 (CV = 18.92%) after intensity normalization. With regards to pseudo-CT synthesis from MRI, the differences in mean values between pseudo-CT and real CT were 3, 15, and 12 HU for soft tissue, fat, and lung/air in 0.35 T chest imaging, respectively, while the corresponding results were 3, 14, and 15 HU in 1.5 T abdominal imaging. Overall, the proposed workflow is reliable in pseudo-CT synthesis from MRI and is more practicable in clinical routine practice compared with deep learning methods, which demand a high level of resources for building a conversion model.

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Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Sensors ◽

10.3390/s21123953 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3953

Author(s):

Han Pu ◽

Tianqiang Huang ◽

Bin Weng ◽

Feng Ye ◽

Chenbin Zhao

Keyword(s):

Detection Method ◽

Real Life ◽

Vital Role ◽

Video Forensics ◽

Flow Algorithm ◽

Benchmark Datasets ◽

Media Reports ◽

Intensity Normalization ◽

Inter Frame ◽

Stable Feature

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

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