Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm

Kumar, S. N.; Fred, A. Lenin; Kumar, H. Ajay; Varghese, P. Sebastin

doi:10.1007/s00034-019-01152-8

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…They use a new image compression algorithm based on a non-uniform partition and U-system designed for such devices. In their work [17], Kumar et al propose lossless compression of CT images by an improved prediction scheme using the least square algorithm. Their implementation on Raspberry Pi shows a significant improvement compared to other algorithms, such as JPEG, JPEG lossless, BCOT, SPIHT, and Maxshift.…”

Section: Related Workmentioning

confidence: 99%

MIDOM—A DICOM-Based Medical Image Communication System

Pervan,

Tomic,

Ivandic

et al. 2023

Applied Sciences

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Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.

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Section: Related Workmentioning

confidence: 99%

MIDOM—A DICOM-Based Medical Image Communication System

Pervan,

Tomic,

Ivandic

et al. 2023

Applied Sciences

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“…Researchers in the past suggested many approaches for image compression [21], [22], [23] especially for medical image compression in telemedicine applications using Neural Networks [24], [25], [26], [27], [28], [29], [30], [31] and few works suggested using fuzzy logic [32], [33]. All these suggested approaches have their own advantages and disadvantages.…”

Section: Literature Reviewmentioning

confidence: 99%

Biomedical Image Compression Techniques for Clinical Image Processing

Saudagar

2020

Int. J. Onl. Eng.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

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“…Recently, deep learning networks have been used to compress images which achieved better compression rates. Furthermore, the use of deep learning models offers better flexibility in terms of the types of objects in the compressed images [3]- [6]. The compression method just has to be trained to new features because these networks do not rely on hand-crafted features.…”

Section: Introductionmentioning

confidence: 99%

A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

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Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm

Cited by 12 publications

References 24 publications

MIDOM—A DICOM-Based Medical Image Communication System

MIDOM—A DICOM-Based Medical Image Communication System

Biomedical Image Compression Techniques for Clinical Image Processing

A novel compression methodology for medical images using deep learning for high-speed transmission

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