Medical Video Coding Based on 2nd Generation Wavelets: Performance Evaluation

Ferroukhi, Merzak; Ouahabi, Abdeldjalil; Attari, Mokhtar; Habchi, Yacine; Bahaj, Mohamed; Taleb-Amed, Abdelmalik

doi:10.20944/preprints201810.0118.v1

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…As for distorted video generation, we use HEVC [ 28 , 29 ] to encode the reference stereo videos. This distorted stereo video generation method is mainly based on two considerations.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Luo

et al. 2021

Entropy

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Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.

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“…As for distorted video generation, we use HEVC [ 28 , 29 ] to encode the reference stereo videos. This distorted stereo video generation method is mainly based on two considerations.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Luo

et al. 2021

Entropy

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“…The latter is obtained by the adjustment of the image intensity values such that 1% of data is saturated at low and high intensities, which sometimes, substantially increases the quality of the raw image. Figures (6 and 7) present the performance metrics in terms of PSNR and SSIM 52 for MRI and X‐Ray texture images of both healthy and osteoporosis cases when the preprocessing stage is through the MF and CE methods.…”

Section: Resultsmentioning

confidence: 99%

Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

Zehani

Ouahabi

Oussalah³

et al. 2020

Int J Imaging Syst Tech

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This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of N8log2N8 where N stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P − value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X‐Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model‐based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.

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“…However, for specific applications that require the extraction of targeted information, it is amply clear that advanced methods will have to be developed and "merged" that exploit existing techniques or optimize analyses (e.g. in compression) by taking into account edges or contours, using 2nd and 3rd generation wavelets such as ridgelets [40], curvelets [41], contourlets [42], bandelets [43], etc. Indeed, these anisotropic wavelets are automatically oriented and expanded by unifying the geometry of a potential edge or contour.…”

Section: Multiresolution Analysis Based On Waveletsmentioning

confidence: 99%

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

Arbaoui¹,

Ouahabi²,

Jacques³

et al. 2021

Preprint

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In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

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Medical Video Coding Based on 2nd Generation Wavelets: Performance Evaluation

Cited by 3 publications

References 28 publications

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

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