Near lossless ROI compression for medical images

JIANG Hui-qin; LI Ping; WANG Zhong-yong; LIU Yu-min

doi:10.3788/OPE.20132103.0759

您当前的位置：

首页 >

文章列表页 >

Near lossless ROI compression for medical images

更新时间：2020-08-12

- Near lossless ROI compression for medical images
- Optics and Precision Engineering Vol. 21, Issue 3, Pages: 759-766(2013)
- 作者机构：
  
  郑州大学信息工程学院和数字化影像技术研究中心
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132103.0759
  CLC： Q-334;TP391.4
- Received：30 October 2012，
  
  Revised：25 December 2012，
  
  Published Online：20 March 2013，
  
  Published：15 March 2013
- 稿件说明：
移动端阅览
蒋慧琴李萍王忠勇刘玉敏. 医学图像感兴趣区域近无损压缩[J]. 光学精密工程, 2013,21(3): 759-766

JIANG Hui-qin LI Ping WANG Zhong-yong LIU Yu-min. Near lossless ROI compression for medical images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 759-766
蒋慧琴李萍王忠勇刘玉敏. 医学图像感兴趣区域近无损压缩[J]. 光学精密工程, 2013,21(3): 759-766 DOI： 10.3788/OPE.20132103.0759.

JIANG Hui-qin LI Ping WANG Zhong-yong LIU Yu-min. Near lossless ROI compression for medical images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 759-766 DOI： 10.3788/OPE.20132103.0759.

摘要

提出了一种基于剪切波的医学图像感兴趣区域(ROI)近无损压缩算法，用于解决因小波对高维数据表示的局限性使其重构图像与原图像平均结构相似度（MSSIM）较低的问题。首先，指定感兴趣区域并把其余部分视为背景区域（BG）；对两个区域分别进行剪切波变换，并选取出能够近似逼近原区域的重要系数进行去噪和初步压缩。然后，对ROI区域所选取的重要系数进行无损Huffman编码，对BG区域所选取的重要系数量化并进行Huffman编码实现压缩。最后，通过Huffman解码和剪切波逆变换实现解压从而获得重构图像。实验结果表明，与改进多级树集合分裂算法（SPIHT）相比，在相同压缩比下，提出的算法所获取的ROI重构图像与原图像ROI的MSSIM提高了4%，峰值信噪比 (PSNR)是改进SPIHT算法的2.35倍；而整幅重构图像与原图像的MSSIM提高了3%，PSNR提高了28%。该算法可实现ROI 和BG 的相对质量可调，适用于图像存档和通信系统（PACS）中的医学图像压缩。

Abstract

A near lossless Region of Interest（ROI)compression algorithm based on the shearlet transform was proposed for medical images to improve the Mean Structural SIMilarity(MSSIM) between the original image and the reconstructed image. Firstly

the ROI was designated in a medical image and the rests were regard as the Background (BG). Then

the ROI and BG were transformed into shearlet domains respectively

and the significant coefficients which could approximate the original region accurately were selected to be denoised and compressed. Furthermore

the main coefficients in ROI were coded by lossless Huffman coding and those in BG were quantized and coded by Huffman coding. Finally

the reconstructed image was obtained by Huffman decoding and inverse shearlet transform. Experiment results show that the MSSIM and Peak Signal Noise Ratio (PSNR) between the original ROI and the reconstructed image RIO obtained by the new algorithm has increased by 4 percent and 135 percent respectively as compared to the modified Set Partitioning in Hierarchical Trees (SPIHT) algorithm with the same compression ratio. Moreover

for the whole image

the MSSIM and PSNR increased by 3 percent and 28 percent， respectively. With configurable ROIs and BGs quality

the proposed algorithm is suitable for the medical image compression in the Picture Archiving and Communication System(PACS).

关键词

Keywords

references

DOUKAS C N, MAGLOGIANNIS I,KORMENTZAS G. Medical image compression using wavelet transform on mobile devices with roi coding support[C].Proceedings of the 2005 27th Annual Conference on Engineering in Medicine and Biology, Shanghai, China: IEEE, 2005: 3779-3784.[2]杨晓,杨学友,叶声华.低码率下任意形状感兴趣区域码[J]. 光学精密工程, 2012,20（4）:896-905.YANG X,YANG X Y,YE SH H. Arbitry shape ROI image encoding at low bit rate[J].Opt. Precision Eng., 2012,20 (4): 896-905. (in Chinese)[3]徐勇,徐智勇,张启衡.基于游程和扩展指数哥伦布编码的任意形状感兴趣区域图像编码[J]. 光学精密工程, 2011,19(1):175-182.XU Y, XU ZH Y,ZHANG Q H. Arbitrary shaped ROI image coding using Run-length coding and generalized Exp-Golomb coding[J]. Opt. Precision Eng., 2011, 19(1):175-182. (in Chinese)[4]徐勇,徐智勇,张启衡,等.嵌入掩模的SPIHT任意形状ROI编码[J]. 光电工程, 2009,36（9）:118-124.XU Y, XU ZH Y, ZHANG Q H, et al.. A mask embedded spiht algorithm for arbitrary shape roi coding[J]. Opto-Electronic Engineering, 2009,36（9）:118-124. (in Chinese)[5]JOAN B R,JOAN S S,FRANCESC A L.JPEG2000 ROI coding with fine-grain accuracy though rate-distortio optimization techniques[J]. IEEE Signal Processing Letters, 2009,16(1):45-48.[6]PABLO G T,RAMON V,MARIA J, et al.. Image compression: maxshift roi encoding options in JPEG2000[J]. ELSEVIER Computer Vision and Image Understanding, 2008,109 (2): 139-145.[7]ZHENG G L, ZHI C, PING X. ROI Auto-detecting and coding method for MRI images transmission[C]. International Conference on Complex Medical Engineering, Tempe, AZ, USA: IEEE, 2009: 1-4.[8]K.V.S. Implementation of prioritized ROI coding for medical image archiving using JPEG2000[C]. International Conference on Signals and Electronic Systems, Krakow, Poland:IEEE, 2008: 239-242.[9]徐向民，刘伟，邢晓芬, 等.基于改进SPIHT算法的医学图像ROI编码[J]. 计算机工程与应用, 2006, 42(8): 207-209.XU X M,LIU W,XING X F, et al.. Medical image roi compression based on the improveed spiht[J].Computer Engineering and Applcations, 2006, 42(8):207-209. (in Chinese)[10]赵志英，王景中，于德海.基于整数小波变换和SPIHT的ROI医学图像压缩[J]. 北方工业大学学报，2005,17(3):10-14.ZHAO ZH Y,WANG J ZH,YU D H. Medical image roi compression based on iwt and spiht\[J\]. North China Univ.of Tech, 2005,17(3):10-14. (in Chinese)[11]RAJKUMAR T M P, MRITYUNJAYA V L. ROI based encoding of medical images:an effective scheme using lifting wavelets and spiht for telemedicine\[J\]. International Journal of Computer Theory and Engineering, 2011, 3(3): 338-346.[12]GUO K, LABTE D. Optimally sparse multi-dimensional representation using shearlets[J]. SIAM J.Math.Anal, 2007, 39(1): 298-318. [13]李萍,蒋慧琴,王忠勇,等.一种基于剪切波变换的医学图像压缩方法[C]. 第十六届全国图象图形学学术会议暨第六届立体图象技术学术研讨会.北京：清华大学出版社，2012：834-838.LI P,JIANG H Q,WANG Z Y, et al.. A shearlet-based medical image compression method[C]. The 16th National Conference on Image and Graphics and The 6th Workshop on 3D Images. Beijing: Tsinghua University Press, 2012:834-838. (in Chinese)[14]WANG Q L. The discrete shearlet transform: A new dir-ectional transform and compactly supported shearlet frames[J]. IEEE Transactions on image processing, 2010, 19 (5): 1166-1180.[15]GLENN E,DEMETRIO L,WANG Q L. Optimally sparse image representations using shearlets[C]. Fortieth Asilomar Conference on Signals, Systems and Computer, Pacific Grove, CA, USA: IEEE, 2006: 974-978 .[16]ZHOU W, ALAN C B, HAMID R S, et al.. Image quality assessment: from error visibility to structyal similarity[J]. IEEE Transactions on Image Processing, 2004,13(4):600-612.

Views

146

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image compression algorithm for fuze radome deformation images

PET/CT Cross-modal medical image fusion of lung tumors based on DCIF-GAN

Efficient coding method for deep space detection Bayer pattern image

Medical image registration based on salient texture

Detection of infrared dim small target based on image patch contrast

Related Author

ZHANG Haoyang

WANG Fan

SHAO Senmu

TIAN Hui

CHEN Rui

ZHOU Tao

CHENG Qianru

ZHANG Xiangxiang

Related Institution

Xi’an Institute of Electromechanical Information Technology

School of Opto-electronical Engineering， Xi’an Technological University

Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission， North Minzu University

School of Medical Information and Engineering， Ningxia Medical University

School of Computer Science and Engineering， North Minzu University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰