A lossless compression method for multi-component medical images based on big data mining.
A lossless compression method for multi-component medical images based on big data mining.
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DOI：
10.1038/s41598-021-91920-x
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发表时间：
2021-06-11
影响因子：
4.6
通讯作者：
Fan P
中科院分区：
文献类型：
--
作者：
Xin G;Fan P
在疾病诊断中，医学影像起着重要的作用。它的无损压缩非常关键，直接决定了远程医疗系统对本地存储空间和通信带宽的需求，以帮助患者的诊断和治疗。医学图像有两个特殊的性质：无损和相似性。如何利用这两个特性来减少图像所需的信息量是压缩的关键。本文采用大数据挖掘的方法建立图像码书。也就是说，找到图像的基本组成部分。提出了一种多分量医学图像的软压缩算法，该算法能够准确地反映图像的基本结构。实验结果表明，本文提出的软压缩算法在压缩比上优于目前流行的PNG和JPEG2000标准。
In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.
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影响因子：
4.6
作者：
Gulyaeva M;Huettmann F;Shestopalov A;Okamatsu M;Matsuno K;Chu DH;Sakoda Y;Glushchenko A;Milton E;Bortz E
通讯作者：
Bortz E
影响因子：
1.4
作者：
Arnavut, Z
通讯作者：
Arnavut, Z
影响因子：
3.7
作者：
ASCHER, RN;NAGY, G
通讯作者：
NAGY, G
影响因子：
6
作者：
Hussain, A. J.;Al-Fayadh, Ali;Radi, Naeem
通讯作者：
Radi, Naeem
DOI：
10.1109/jrproc.1952.273898
发表时间：
1952-01-01
影响因子：
--
作者：
HUFFMAN, DA
通讯作者：
HUFFMAN, DA