Share：
Share this content in WeChat
X
[Chinese][PDF]773139
• Review •
Research progress on lossy compression techniques for medical images
LIU Yu  WANG Qian-nan  NIAN Yong-jian  QIU Ming-guo 

DOI:10.12015/issn.1674-8034.2016.06.015.


[Abstract] Medical imaging has been the important basis for the medical diagnosis and treatment. With the resolution of medical imaging devices, the data amount of acquired medical images increases consistently, which creates heavy burden for image storage and transmission and seriously restricts the following application of medical images. The manner of lossy compression can realize the high degree compression for medical images under the constraint of certain image quality, which has been the research focus both at home and abroad. In this paper, the research progress on lossy compression for medical images is summarized, and the basic methods of quality evaluation under the condition of lossy compression are described. Finally, the development trend of lossy compression for medical images is expected.
[Keywords] Medical image；Magnetic resonance imaging；Lossy compression；Region of interest；Quality evaluation

LIU Yu The Second Squad of the 4th Platoon, the 19th Student Battalion, Third Military Medical University, Chongqing 400038, China

WANG Qian-nan The First Squad of the 5th Platoon, the 19th Student Battalion, Third Military Medical University, Chongqing 400038, China

NIAN Yong-jian* Department of Medical Images, School of Biomedical Engineering, Third Military Medical University, Chongqing 400038, China

QIU Ming-guo Department of Medical Images, School of Biomedical Engineering, Third Military Medical University, Chongqing 400038, China

*Correspondence to: Nian YJ, E-mail: yjnian@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Natural Science Foundation of China No. 41201363, 81171866
Received  2016-02-18
Accepted  2016-04-11
DOI: 10.12015/issn.1674-8034.2016.06.015
DOI:10.12015/issn.1674-8034.2016.06.015.

[1]
Wu D, Tan DM, Baird M, et al. Perceptually lossless medical image coding. IEEE Transactions on Medical Imaging, 2006, 25(3): 335-344.
[2]
Bruckmann A, Uhl A. Selective medical image compression techniques for telemedical and archiving applications. Computers in Biology and Medicine, 2000, 30(3): 153-169.
[3]
Sophia PE, Anitha J. Implementation of region based medical image compression for telemedicine application. Proceedings of the International Conference on Computational Intelligence and Computing Research, Coimbatore, India, 2014: 1-4.
[4]
Schelkens P, Munteanu A, Cornelis J. Wavelet-based compression of medical images: protocols to improve resolution and quality scalability and region-of-interest coding. Future Generation Computer Systems, 1999, 15(2): 171-184.
[5]
Tahoces PG, Varela JR, Lado MJ, et al. Image compression: maxshift ROI encoding options in JPEG2000. Computer Vision and Image Understanding, 2008, 109(2): 139-145.
[6]
Sridhar KV. Implementation of prioritised ROI coding for medical image archiving using JPEG2000. Proceedings of the International Conference on Signals and Electronic Systems, Krakow, Poland, 2008: 239-242.
[7]
Hu M, Zhang C, Lu J, et al. A multi-ROIs medical image compression algorithm with edge feature preserving. Proceedings of the International Conference on Intelligent System and Knowledge Engineering, Xiamen, China, 2008: 1075-1080.
[8]
Firoozbakht M, Dehmeshki J, Martini M, et al. Compression of digital medical images based on multiple regions of interest. Proceedings of the International Conference on Digital Society, St. Maarten, Holland, 2010: 260-263.
[9]
Bartrina-Rapesta J, Serra-Sagristà J, Aulí-Llinàs F. JPEG2000 ROI coding through component priority for digital mammography. Computer Vision and Image Understanding, 2011, 115(1): 59-68.
[10]
Kaur M, Wasson V. ROI based medical image compression for telemedicine application. Proceedings of the International Conference on Eco-friendly Computing and Communication Systems, Kurukshetra, Haryana, 2015: 579-585.
[11]
Hosseini SM, Naghsh-Nilchi AR. Medical ultrasound image compression using contextual vector quantization. Computers in Biology and Medicine, 2012, 42(7): 743-750.
[12]
Rahmiati P, Fajri A, Handayani A, et al. Distributed system for medical image transfer using wavelet-based dynamic ROI coding. Proceedings of the International Workshop on Enterprise networking and Computing in Healthcare Industry, Busan, Korea, 2005: 191-196.
[13]
Doukas C, Maglogiannis I. Region of interest coding techniques for medical image compression. IEEE Engineering in Medicine and Biology Magazine, 2007, 26(5): 29-35.
[14]
Doukas CN, Maglogiannis I, Kormentzas G. Medical image compression using wavelet transform on mobile devices with ROI coding support. Proceedings of the IEEE Engineering in Medicine and Biology, Shanghai, China, 2005: 3779-3784.
[15]
Agrafiotis D, Bull DR, Canagarajah N. Region of interest coding of volumetric medical images. Proceedings of the International Symposium on Image and Signal Processing and Analysis, 2003, 3: 217-220.
[16]
Wang K, Cuhadar A. Unbalanced 3-D tree structure for region-based coding of volumetric medical images. Proceedings of the International Conference on Engineering in Medicine and Biology Society, New York, USA, 2006: 3286-3289.
[17]
Sanchez V, Abugharbieh R, Nasiopoulos P. 3-D scalable medical image compression with optimized volume of interest coding. IEEE Transactions on Medical Imaging, 2010, 29(10): 1808-1820.
[18]
Sanchez V. Joint source/channel coding for prioritized wireless transmission of multiple 3-D regions of interest in 3-D medical imaging data. IEEE Transactions on Biomedical Engineering, 2013, 60(2): 397-405.
[19]
Nguyen BP, Chui CK, Ong SH, et al. An efficient compression scheme for 4-D medical images using hierarchical vector quantization and motion compensation. Computers in Biology and Medicine, 2011, 41(9): 843-856.
[20]
侯阿临，张利红，徐坤. 基于EBCOT的ROI医学图像多描述编码方法研究. 电视技术, 2008, 32(S1): 37-47.
[21]
许向阳，丁涛，宋恩民. 基于SPIHT的感兴趣区域编码. 计算机工程与设计, 2007, 28(21): 5144-5146.
[22]
汤敏，陈秀梅，陈峰. 基于Contourlet变换和SPIHT算法的彩色医学图像压缩. 计算机科学, 2014, 41(1): 303-306.
[23]
陈秀梅，王伟，汤敏. 基于Curvelet变换和SPIHT算法的医学图像感兴趣区压缩. 中国医学影像学杂志, 2014, 22(10): 786-792.
[24]
孔祥海，张媛，梁艳梅. 基于压缩感知的医学图像采样新方法研究. 光电子·激光, 2014, 25(8): 1635-1640.
[25]
王超，王浩，王伟, 等. 基于优化ROI的医学图像分割与压缩方法研究. 重庆邮电大学学报, 2015, 27(2): 279-284.
[26]
Sridhar KV, Prasad KSRK. Medical image compression using advanced coding technique. Proceedings of the International Conference on Signal Processing, Beijing, China, 2008: 2142- 2145.
[27]
Prabhu KMM, Sridhar K, Mischi M, et al. 3-D warped discrete cosine transform for MRI image compression. Biomedical Signal Processing and Control, 2013, 8(1): 50-58.
[28]
Bhavani S, Thanushkodi KG. Comparison of fractal coding methods for medical image compression. IET Image Processing, 2013, 7(7): 686-693.
[29]
Juliet S, Rajsingh EB, Ezra K. A novel medical image compression using Ripplet transform. Journal of Real-Time Image Processing, 2013, 11(2): 401-412.
[30]
Juliet S, Rajsingh EB, Ezra K. A novel image compression method for medical images using geometrical regularity of image structure. Signal, Image and Video Processing, 2015, 9(7): 1691-1703.
[31]
Juliet S, Rajsingh EB, Ezra K. Projection-based medical image compression for telemedicine applications. Journal of Digital Imaging, 2015, 28(2): 146-159.
[32]
Selvi GUV, Nadarajan R. A rapid compression technique for 4- D functional MRI images using data rearrangement and modified binary array techniques. Australasian Physical & Engineering Sciences in Medicine, 2015, 38(4): 731-742.
[33]
Basarab A, Achim A, Kouamé D. Medical ultrasound image reconstruction using compressive sampling and norm minimization. Proceedings of SPIE, 2014, 9040. DOI: 10.1117/12.2042991.
[34]
Imen M, Fayez K, Khalil H, et al. Mask motion adaptive medical image coding. Proceedings of IEEE-EMBS International Conference on Biomedical and Health Informatics, Valencia, Spain, 2014: 408-411.
[35]
Kowalik-Urbaniaka I, Bruneta D, Wang JH, et al. The quest for "diagnostically lossless" medical imagecompression: a comparative study of objective qualitymetrics for compressed medical images. Proceedings of SPIE, 2014, 9037: 1-16.
[36]
Shah RR, Sharma P. Performance analysis of region of interest based compression method for medical images. Proceedings of the International Conference on Advanced Computing & Communication Technologies, Rohtak, India, 2014: 53-58.
[37]
Kocsis O, Costaridou L, Mandellos G, et al. Compression assessment based on medical image qualityconcepts using computer-generated test images. Computer Methods and Programs in Biomedicine, 2003, 71(2): 105-115.
[38]
刘玉，崔皓然，粘永健, 等. 医学图像无损压缩技术研究进展.磁共振成像, 2016, 7(2): 149-155.

PREV Progress in research application of magnetic resonance in rapid eye movement sleep behavior disorder
NEXT A study of acupuncture Dazhong and Taixi acupointsbased on fMRI
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn