| Literature DB >> 35342283 |
S Boopathiraja1, V Punitha1, P Kalavathi1, V B Surya Prasath2,3,4,5.
Abstract
In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last two decades, numerous compression mechanisms, techniques and algorithms were proposed by many researchers. This work provides a detailed status of these existing computational compression methods for medical imaging data. 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.Entities:
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
Year: 2021 PMID: 35342283 PMCID: PMC8942405 DOI: 10.1007/s11831-021-09602-w
Source DB: PubMed Journal: Arch Comput Methods Eng ISSN: 1134-3060 Impact factor: 7.302