A Fast Method of Visually Lossless Compression of Dental Images

Krivenko, Sergey; Krylova, O.; Kryvenko, Liudmyla; Егиазарян, Карен

doi:10.3390/app11010135

Cited by 14 publications

(8 citation statements)

References 36 publications

(57 reference statements)

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“…In the test dataset (N = 12 volumes, including MRI and PET scans), the authors report CR starting at 11:1 up to 37:1, minimum PSNR of 42 dB and minimum SSIM of 0.95, approximately. Another initiative aimed to provide visually lossless image compression was proposed in [24], specifically for dental orthopantomography images. The authors claim that neither artifacts or loss of diagnostically valuable information were induced with the proposed lossy compression solution, which was able to compress the data from 8:1 to 21:1 with a minimum PSNR of 39 dB.…”

Section: Related Workmentioning

confidence: 99%

Lossy Image Compression in a Preclinical Multimodal Imaging Study

et al. 2023

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The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

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Section: Related Workmentioning

confidence: 99%

Lossy Image Compression in a Preclinical Multimodal Imaging Study

et al. 2023

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“…eq varies from 30 to almost 200 (for the fragment in Figure 2B) (Krivenko et al, 2020). In any case, the noise occurs to be visible.…”

Section: Methodology Of Dental Image Receiving Analysis and Basic Mag...mentioning

confidence: 86%

“…Both approaches will be further analyzed and discussed in the remainder part of this paper. The latter one has been intensively studied in our recent papers (Krivenko et al, 2020;Krylova et al, 2021;Kryvenko et al, 2022) for three different DCT-based coders, namely, ADCTC (advanced DCT coder) (Ponomarenko et al, 2007), AGU-M (Zemliachenko et al, 2016), and better portable graphics (BPG) (BPG Image format, 2022) encoders, respectively. It has been shown that by setting a proper PCC [QS for the ADCTC, scaling factor (SF) for the AGU-M coder, and parameter Q for the BPG encoder] it is possible to provide the metric PSNR-HVS-M (Ponomarenko et al) in the range 40 .…”

Section: Introductionmentioning

confidence: 99%

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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“…The above claim has not been sufficiently investigated, and there is a possibility that the experimental way which defines the thresholds for visual perception without losses is insufficient to guarantee diagnostic performance. Challenging tasks, including lowcontrast detection, must maintain high-frequency information, or they will be vulnerable to high compression rates, which are misinterpreted as false-positive findings [48].…”

Section: Medical Image Compression Evaluation Methodsmentioning

confidence: 99%

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

et al. 2023

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Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.

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A Fast Method of Visually Lossless Compression of Dental Images

Cited by 14 publications

References 36 publications

Lossy Image Compression in a Preclinical Multimodal Imaging Study

Lossy Image Compression in a Preclinical Multimodal Imaging Study

Intelligent visually lossless compression of dental images

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

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