字典学习中字典尺度对DICOM图像压缩的影响

酉霞; 陈菲; 贾小林; 刘雨娇; 杨勇

doi:10.3788/YJYXS20153006.1045

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字典学习中字典尺度对DICOM图像压缩的影响

成像技术与图像处理 | 更新时间：2020-08-12

- 字典学习中字典尺度对DICOM图像压缩的影响
- Effects of dictionary scale on dictionary learning for DICOM image compression
- 液晶与显示 2015年30卷第6期页码：1045-1051
- 作者机构：
  
  1. 西南科技大学计算机科学与技术学院, 四川绵阳 621010
  2. 四川省绵阳市中心医院, 四川绵阳 621000
- 作者简介：
  
  [ "酉霞(1990-),女,四川简阳人,硕士研究生,主要研究方向:数字图像处理,机器学习。E-mail:youzi_2011@yeah.net" ]
  [ "陈菲(1974-),女,四川绵阳人,副教授,硕士研究生导师,主要研究方向:嵌入式系统技术及图像处理" ]
  [ "贾小林(1975-),男,四川绵阳人,副教授,博士,主要研究方向:数据采集与识别技术" ]
  [ "刘雨娇(1991-),女,四川绵阳人,硕士研究生,主要研究方向:计算机视觉" ]
  [ "杨勇(1974-),男,四川绵阳人,主治医师,学士,主要研究方向:影像技术方向。" ]
- 基金信息：
  
  国家自然科学基金面上项目(No.61471306)();四川省科技厅项目(No.16ZC1720,No.2014JY0230)();西南科技大学研究生创新基金(No:1
- DOI：10.3788/YJYXS20153006.1045
  中图分类号： TP391.41
- 收稿日期：2015-01-22，
  
  修回日期：2015-03-24，
  
  纸质出版日期：2015-12-05
- 稿件说明：
移动端阅览
酉霞, 陈菲, 贾小林等. 字典学习中字典尺度对DICOM图像压缩的影响[J]. 液晶与显示, 2015,30(6): 1045-1051

YOU Xia, CHEN Fei, JIA Xiao-lin etc. Effects of dictionary scale on dictionary learning for DICOM image compression[J]. , 2015,30(6): 1045-1051
酉霞, 陈菲, 贾小林等. 字典学习中字典尺度对DICOM图像压缩的影响[J]. 液晶与显示, 2015,30(6): 1045-1051 DOI： 10.3788/YJYXS20153006.1045.

YOU Xia, CHEN Fei, JIA Xiao-lin etc. Effects of dictionary scale on dictionary learning for DICOM image compression[J]. , 2015,30(6): 1045-1051 DOI： 10.3788/YJYXS20153006.1045.

摘要

随着医院数字化医疗进程的加快

医学影像的数据量日益增大

医学影像资料的存储空间和获取速度受到很大的限制。文章在研究主流字典学习算法基础上

提出使用不同尺度的MOD、K-SVD、ILS-DLA、RLS-DLA字典算法对DICOM图像进行压缩存储

以及恢复再现的方法。与经典的JPEG和JPEG2000压缩算法相比

字典学习算法压缩和恢复效果较好

特别是采用较小尺度的字典时

压缩效果更为突出:当压缩比为20时

采用44尺度的RLS-DLA字典

论文算法的峰值信噪比(PSNR)较JPEG算法高出7.8 dB

比JPEG2000算法高出1 dB。

Abstract

With the accelerated developing of hospital digital medical

the amount of medical imaging data grows dramatically

which affects the data storage space and access speed. This paper proposes a new design which uses different scales dictionaries of MOD

K-SVD

ILS-DLA

RLS-DLA for digital imaging and communications in medicine (DICOM) image compression storage and restore methods based on dictionary learning. Compared with the traditional algorithms JPEG and JPEG2000

the proposed method has better performance

especially when the dictionary scale is smaller. For example

when the compression ratio is 20

using 44 dictionary scale

the peak signal to noise ratio (PSNR) of the proposed method is 7.8 dB higher than that of JPEG

and 1dB than JPEG2000.

关键词

Keywords

references

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张健,李宏升.基于图论阈值算法的图像分割研究[J].液晶与显示, 2014,29(4):592-597. Zhang J, Li H S.Image mosaic research based on wavelet and rough set algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2014,29(4):592-597. (in Chinese)

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