An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

Li, Zhongqiang; Ramos, Alexandra; Li, Zheng; Osborn, Michelle L.; Li, Xin; Li, Yanping; Yao, Shaomian; Xu, Jian

doi:10.1016/j.bspc.2020.102306

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Beyond segmentation and classification, DL techniques have also been applied successfully to medical image compression in recent years. This is especially the case for computerized tomography (CT) and magnetic resonance (MR) images, which have traditionally been compressed using transform coding methods such as JPEG and HEVC [18][19][20][21][22]. Despite the promising results with CT and MR images, the field of ultrasound image compression based on DL methods remains relatively unexplored.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Gencol,

Gungor

2023

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Breast cancer, a pervasive and life-threatening malignancy, predominantly affects women worldwide. Despite the widespread adoption of imaging technologies such as mammography for early-stage breast cancer detection, access to such specialized imaging equipment remains limited in low-income countries. Conversely, ultrasound imaging has demonstrated its efficacy as a cost-effective tool for tumor identification. The advent of portable ultrasound devices facilitates rapid and precise lesion diagnosis in the breast, circumventing the need for hospital visits. Nevertheless, the images procured by portable ultrasound devices are typically necessitated to be transmitted in a compressed format for remote evaluation by physicians. This compression process often introduces artifacts in medical images, complicating the delineation of tumorous regions. To address this challenge, we introduce a deep-learning solution in this paper. A novel wavelet convolutional neural network (CNN) architecture is conceived to learn and subsequently diminish the artifacts present in compressed ultrasound images. To achieve this, a diverse dataset comprising various types of breast ultrasound imagesmalignant, benign, and normalis utilized. Experimental outcomes indicate that the proposed method surpasses the denoising CNN in mitigating artifacts in compressed ultrasound images. This improved performance is particularly evident in the most compressed images, which are of significant interest. This research underscores the potential of deploying deep-learning techniques to enhance the quality of compressed medical images, thereby facilitating more accurate and efficient remote diagnoses.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Gencol,

Gungor

2023

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“…Commonly used lossy compression schemes include JPEG-2000 [14,15], JPEG [16], and so on. However, until now, lossy compression approaches are less acceptable in the field of medical imaging because they lead to the loss of critical information details in medical images [17,18]. This is why lossless compression techniques are more appropriate to the medical imaging field, as they avoid any visual information loss while reducing the size of the image file [13,19].…”

Section: Introductionmentioning

confidence: 99%

“…However, these predictor-based algorithms are independent of the image compression standard (JPEG) structure [22]. To support the JPEG standard, lossless compression schemes have been introduced in the transform coding domain [18,23,24]. Such schemes are essentially based on the use of the integer discrete cosine transform (IDCT), which was introduced to the field of image processing about 23 years ago [25].…”

Section: Introductionmentioning

confidence: 99%

Novel Integer Shmaliy Transform and New Multiparametric Piecewise Linear Chaotic Map for Joint Lossless Compression and Encryption of Medical Images in IoMTs

Daoui,

Mao,

Yamni

et al. 2023

Mathematics

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The discrete Shmaliy moment transform (DST) is a type of discrete orthogonal moment transform that is widely used in signal and image processing. However, DST is not suitable for lossless image applications due to its non-integer reversible nature. To overcome this limitation, we introduce the integer discrete Shmaliy transform (IDST) that performs integer-to-integer encoding, leading to a perfect and unique reconstruction of the input image. Next, a new 1D chaotic system model, the 1D multiparametric piecewise linear chaotic map (M-PWLCM), is presented as an extension of the existing 1D PWLCM. The M-PWLCM includes eight control parameters defined over an unlimited interval. To demonstrate the relevance of IDST and M-PWLCM in reversible image processing applications, they are used in a new scheme for lossless compression and encryption of medical images in the internet of medical things (IoMTs). On the one hand, the simulation results show that our scheme offers a good compression ratio and a higher level of security to resist differential attacks, brute force attacks and statistical attacks. On the other hand, the comparative analysis carried out shows the overall superiority of our scheme over similar state-of-the-art ones, both in achieving a higher compression ratio and better security when communicating medical images over unsecured IoMTs.

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“…Additionally, the viewer focuses more on the image content in the regions of interest (ROI) and is indifferent to the background regions (BG). The generic compression method, like JPEG, and its improvements, mostly focus on compression efficiency and linear transformation methods (Li et al, 2021; Retraint & Zitzmann, 2020), which are not perfectly applicable to the needs of greenhouse imaging.…”

Section: Introductionmentioning

confidence: 99%

A sub‐regional compression method for greenhouse images based on CNN image quality assessment

Wen

et al. 2022

Food Processing Preservation

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As a major Internet of Things information carrier within the greenhouse, image quality and image file size need to be balanced. To reduce image file size while maintaining image quality, a sub‐regional compression (SRC) method is proposed, and its optimal compression quality of the background regions is selected using image quality assessments such as SSIM, BRISQUE, NIQE, and PIQE. In addition, a convolutional neural network‐based NIMA model to measure image quality is also being introduced. In the end, the SRC method is compared with JPEG compression default mode and JPEG compression with quality 30 in NIMA score, compression time, and compression efficiency, proving the effectiveness of the method. Practical applications Certain vegetables and fruits, such as cucumbers, tomatoes, etc., in most cases are processed directly into the hands of consumers, so the loss is directly attributable to their growth, especially pests and diseases. Monitoring for the above situation is a great improvement, but the increase in monitoring accuracy brings video/key frame to becoming larger. Therefore, it is better to reduce the file size as well as to ensure clarity in regions of interest, viewing the files faster and detecting the losses by humans or machines as easily as possible. This paper investigates the overall process and innovatively introduces deep learning into image quality assessment to better simulate human perception of images, and the research results of this paper can be applied to different models of surveillance cameras to achieve the same results.

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An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

Cited by 11 publications

References 20 publications

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Novel Integer Shmaliy Transform and New Multiparametric Piecewise Linear Chaotic Map for Joint Lossless Compression and Encryption of Medical Images in IoMTs

A sub‐regional compression method for greenhouse images based on CNN image quality assessment

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