Analysis of Noise Properties in Dental Images

Abramova, Victoriya; Krivenko, Sergey; Krylova, O.

doi:10.1109/elnano50318.2020.9088768

Cited by 5 publications

(14 citation statements)

References 16 publications

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“…The dental images acquired by the modern imaging systems are large in size, at least one megapixel data [12,13,31]. In particular, this relates to the images acquired by the system Morita, panoramic X-ray (Veraviewepocs 3D R100 J) [37].…”

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

“…There is a noise present in dental images, as one can see from these fragments, that might partly mask important diagnostic information. This noise arises due to a limited time of imaging the object, and properties of noise are complex [29,31,38]. In the simplest case, one can model noise in dental images as Poisson-like signal dependent [29].…”

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

“…However, more detailed analysis shows that it is, in general, signal dependent but has more complex nature. It has a signal-independent (SI) component and a signal-dependent (SD) component, variance of which can be proportional to a signal intensity [31]. There is a noise present in dental images, as one can see from these fragments, that might partly mask important diagnostic information.…”

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

“…, where I ij is a true intensity value of an ij-th image pixel (i = 1, N, j = 1, M, N and M denote vertical and horizontal image sizes, respectively);σ 2 µ is an estimate of multiplicative noise relative variance;σ 2 a is an estimate of additive noise variance;k is a quasi-Poisson noise parameter. Experiments in [31] show that the simple modelsσ 2…”

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

“…Medical images, in general, and dental images, in particular, can be corrupted by a noise [28][29][30], which can be seen well in the visualized data. Noise properties depend on many factors and, in general, are not purely additive and white [29][30][31]. Moreover, a lossy image compression had several peculiarities discovered in [32,33] and was considered later in the literature [34,35].…”

Section: Introductionmentioning

confidence: 99%

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A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

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Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

Section: Some Assumptions On Image/noise Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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Determination of the risk of developing diabetes mellitus based on the patterns of symbolic dynamics of the amplitude-time heart rate variability

Pulavskyi

Linskiy

Posokhov

et al. 2021

2021 10th Mediterranean Conference on Embedded Computing (MECO)

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Intelligent visually lossless compression of dental images

Kryvenko,

Krylova,

Lukin

et al. 2024

Adv. Opt. Technol.

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Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio.Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on.Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear.Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists).Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.

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Analysis of Noise Properties in Dental Images

Cited by 5 publications

References 16 publications

A Fast Method of Visually Lossless Compression of Dental Images

A Fast Method of Visually Lossless Compression of Dental Images

Determination of the risk of developing diabetes mellitus based on the patterns of symbolic dynamics of the amplitude-time heart rate variability

Intelligent visually lossless compression of dental images

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