Boopathiraja S, Punitha V, Kalavathi P, Surya Prasath V B
Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, 624 302 Tamil Nadu, India.
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, OH 45229 USA.
Arch Comput Methods Eng. 2022 Mar;29(2):975-1007. doi: 10.1007/s11831-021-09602-w. Epub 2021 May 7.
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.
在这个大数据的时代,医学技术的开发与应用正在迅猛增长,尤其是在医学领域广泛使用的大型生物医学成像模式方面。与此同时,如此海量的医学数据的采集、处理、存储和传输需要高效且强大的数据压缩模型。在过去的二十年里,许多研究人员提出了众多的压缩机制、技术和算法。这项工作详细介绍了这些现有的医学成像数据计算压缩方法的现状。详细回顾了在二维(2D)和三维(3D)医学图像压缩领域进行适当分类、性能指标、实际问题和挑战。