What is it about?

Currently we produce large-scale data via big biomedical imaging modalities available across medicine. So storing and transmission of such huge medical data requires efficient and robust data compression models. In this paper we extensively review various compression methods developed in the last two decades. Appropriate classification, performance metrics, practical issues and challenges in enhancing the two dimensional (2D) and three dimensional (3D) medical image compression arena are reviewed in detail.

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Why is it important?

Data compression is an important requirement in catering to resource-constraint medical imaging environments. In this work we have shown that various compression models can be effectively used for 2D and 3D medical imaging data.

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This page is a summary of: Computational 2D and 3D Medical Image Data Compression Models, Archives of Computational Methods in Engineering, May 2021, Springer Science + Business Media, DOI: 10.1007/s11831-021-09602-w.
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