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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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COMPUTATIONAL 2D and 3D MEDICAL IMAGE DATA COMPRESSION MODELS.

S Boopathiraja¹, V Punitha¹, P Kalavathi¹

¹Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, 624 302 Tamil Nadu, India.

Archives of Computational Methods in Engineering : State of the Art Reviews

|March 28, 2022

Summary

This summary is machine-generated.

This review details computational compression methods for medical imaging data, addressing the growing need for efficient handling of large biomedical datasets. It classifies techniques and discusses challenges in 2D and 3D medical image compression.

Keywords:

Compression Metrics Computational Imaging Lossless Compression Lossy Compression Medical Image Compression Near-lossless Compression Object based Compression Methods Tensor Based compression Methods Wavelets Based Compression Methods

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Area of Science:

Medical Imaging Technology
Biomedical Data Science
Computational Medicine

Background:

The increasing volume of medical imaging data necessitates efficient compression models for acquisition, processing, storage, and transmission.
Numerous data compression techniques have been developed over the last two decades to manage large biomedical datasets.
Existing computational compression methods for medical imaging data require a comprehensive review and classification.

Purpose of the Study:

To provide a detailed status of existing computational compression methods for medical imaging data.
To classify various compression techniques applicable to medical imaging.
To review practical issues and challenges in enhancing 2D and 3D medical image compression.

Main Methods:

Literature review of computational compression methods for medical imaging.
Classification of existing compression techniques.
Analysis of performance metrics, practical issues, and challenges.

Main Results:

A detailed overview of various computational compression methods for medical imaging data is presented.
Classification of compression techniques based on their methodologies.
Identification of key performance metrics, practical considerations, and challenges in the field.

Conclusions:

Efficient and robust data compression is crucial for managing the expanding volume of medical imaging data.
A systematic review and classification of compression methods are essential for advancing medical image compression.
Addressing practical issues and challenges will enhance the effectiveness of 2D and 3D medical image compression.