Computational 2D and 3D Medical Image Data Compression Models

Boopathiraja, S.; Punitha, V.; Kalavathi, P.; Prasath, V. B. Surya

doi:10.1007/s11831-021-09602-w

Cited by 18 publications

(7 citation statements)

References 89 publications

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“…However, its efficiency varies with image size and complexity, especially for larger 3D MRI images. Block processing methods have also been explored 13 , but they often compromise critical image details, which is not viable for medical diagnostics. A novel index structure was presented to achieve speed up for compressing the data.…”

Section: Related Workmentioning

confidence: 99%

“…The existing literature often overlooks the unique requirements of telemedicine, such as the need for efficient data transmission over limited bandwidth and the necessity to maintain high image fidelity for accurate remote diagnosis. Current methods still grapple with either data loss in lossy compression or insufficient size reduction in lossless methods, limiting their practicality in real-time telemedicine 13 .…”

Section: Related Workmentioning

confidence: 99%

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Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Elhadad,

Jamjoom,

Abulkasim

2024

Sci Rep

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Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Elhadad,

Jamjoom,

Abulkasim

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Performance data is presented using quantitative measurements in the form of numbers. To evaluate the effectiveness of compression techniques statistical measures or tools are used [3]. To assess the audio compression performance measurements like Signal-to-noise ratio (SNR) and Bits Per Second (BPS) were used [10].…”

Section: Related Workmentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

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“…By integrating AI, immersive technologies can become more interactive and responsive to user actions and behaviors immersive technologies and AI, the potential fusion of these technologies may contribute to a more thorough understanding of different aspects of cardiac anatomy during procedures [ 15 ]. They may also influence the selection of the appropriate device and procedural technique, due to better preprocedural planning and real-time intraprocedural visualization for complex anatomical and geometrical relations [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Advancements in artificial intelligence-driven techniques for interventional cardiology

Rudnicka,

Pręgowska,

Glądys

et al. 2024

Cardiol J.

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This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

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Computational 2D and 3D Medical Image Data Compression Models

Cited by 18 publications

References 89 publications

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Survey On Parameters of Data Compression

Advancements in artificial intelligence-driven techniques for interventional cardiology

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