nach oben

Journal of Medical Systems

Erschienen in:

01.08.2012 | ORIGINAL PAPER

Lossless Compression of Threshold-Segmented Medical Images

verfasst von: Denis Špelič, Borut Žalik

Erschienen in: Journal of Medical Systems | Ausgabe 4/2012

Einloggen, um Zugang zu erhalten

Abstract

This paper introduces a new algorithm for losslessly compressing voxel-based 3D CT medical images. Firstly, medical data is segmented according to selected ranges of the Hounsfield scale. The data is then arranged into two data streams. The first stream identifies the positions of the remaining data after segmentation. This information is compressed by the JBIG standard. The second stream contains the exact data values and it is losslessly compressed by our algorithm. The efficiency of this approach has been evaluated by a prototype application. This approach represents an interesting alternative for the long-term storage of medical 2D and 3D images, and for applications in telemedicine. The compression method can be used either for 2D or 3D medical data.

Oppelt, A., Imaging system for medical diagnostics. Publicis Corporate Publishing, Erlangen, 2005.

Maintz, J., and Viergever, M., A survey of medical image registration. Med. Image Anal. 2(1):1–36, 1998.CrossRef

Mortenson, M. E., Geometric modeling. John Wiley & Sons Inc.; 1985.

Dougherty, G., Digital image processing for medical applications. Cambridge University Press, New York, 2009.

Correa, C., and Ma, K. L., The occlusion spectrum for volume classification and visualization. IEEE Trans. Vis. Comput. Graph. 15(6):1465–1472, 2009.CrossRef

Cortes, J., Barbe, S., Erard, M., Simeon, T., Encoding molecular motions in voxel maps. IEEE International Conference on Robotics and Automation; May 12–17; Kobe, Japan, 2009.

Goksel, O., and Salcudean, S. E., B-mode ultrasound image simulation in deformable 3-D medium. IEEE Trans. Med. Imag. 28(11):1657–1669, 2009.CrossRef

Agrawal, A., Kohout, J., Clapworthy, G. J., McFarlane, N. J. B., Dong, F., Viceconti, M., Taddei, F., and Testi, D., Enabling the interactive display of large medical volume datasets by multiresolution bricking. J. Supercomput. 51:3–19, 2010.CrossRef

Dong, F., Clapworthy, J. G., Krokos, M., Volume rendering of fine details within medical data. Proceeding of the Conference on Visualization’01. Washington DC, USA; pp. 387–394, 2001.

10.

DICOM Standards Committee, Digital imaging and communications in medicine, 2006.

11.

Digital Imaging and Communications in Medicine (DICOM), NEMA Publications, “DICOM Standard”, available at: ftp://medical.nema.org/medical/dicom/2008/, 2008.

12.

Ghrare, S. E., Ali, M. A., Jumari, K., and Ismail, M., An efficient low complexity lossless coding algorithm for medical images. Am. J. Appl. Sci. 6(8):1502–1508, 2009.CrossRef

13.

Shannon, C. E., A mathematical theory of communication. Bell Syst. Tech. J. 27:379–423, 1948.MathSciNetMATH

14.

Huffman, D., A method for the construction of minimum redundancy codes. Proc. IRE 40:1089–1101, 1952.CrossRef

15.

Salomon, D., Data compression—The complete reference, 4th edition. Springer, New York, 2007.MATH

16.

Fowler, J. E., Yagel, R., Lossless compression of volume data. Proceedings of the symposium on volume visualization; 1994. Tysons Corner, Virginia; p. 43–50, 1994.

17.

Muraki, S., Volume data and wavelet transforms. IEEE Comput. Graph. Appl. 13:50–56, 1993.CrossRef

18.

Chiueh, T., Yang, C., He, T., Pfister, H., Kaufman, A., Integrated volume compression and visualization. Proceedings of the 8th Conference on Visualization. Phoenix, Arizona; p. 329–336, 1997.

19.

Ma, K. L., Shen, H. W., Compression and accelerated rendering of time-varying volume data. Proceedings of the workshop on computer graphics and virtual reality. Taipei, Taiwan; p. 263–270, 2001.

20.

Saffor, A., Ramli, A., and Ng, K., A Comparative Study of Image Compression Between JPEG and Wavelet. Malays. J. Comput. Sci. 14:39–45, 2001.

21.

Saffor, A., Ramli, A., and Ng, K., Objective and subjective evaluation of compressed computed tomography CT images. Internet J. Radiol. 2:1–5, 2002.

22.

Taubman, D., Marcellin, M., JPEG2000: Image compression fundamentals, standards and practice. Kluwer Academic Publishers, 2002.

23.

Hui, T., and Besar, R., Medical image compression using JPEG2000 and JPEG: A comparative study. World Sci. J. Mech. Med. Biol. 2:313–328, 2002.

24.

Carlender, M. L., and Christopoulos, C., Region of interest coding in JPEG 2000. Signal Process. Image Commun. J. 17:105–111, 2002.CrossRef

25.

Kocsis, O., Costardidou, L., Varaki, L., Kalogeroulou, C., and Panayiotakis, G., Compression assessment based on medical image quality concepts using computer generated test images. Comput. Meth. Programs Biomed. 71:105–115, 2003.CrossRef

26.

Asraf, R., Akbar, M., Jafri, N., Statistical analysis of difference image for absolute lossless compression of medical images. Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA; p. 4767–4770, 2006.

27.

Yao, X., Xiao, T., Mao, S., Image compression based on classification row by row and LZW encoding. Proceedings of the congress on image and signal processing. China; p. 617–621, 2008.

28.

Fang, S., Srinivasan, R., Huang, S., Raghavan, R., Deformable volume rendering by 3D texture mapping and octree encoding. Proceedings of the 7th conference on visualization. San Francisco: California; p. 73–80, 1996.

29.

Guthe, S., Straßer, W., Real time decompression and visualization of animated volume data. Proceedings of the conference on Visualization. San Diego: California; p. 349–356, 2001.

30.

Klajnšek, G., and Žalik, B., Progressive lossless compression of volumetric data using small memory load. Comput. Med. Imaging Graph. 29(4):305–312, 2005.CrossRef

31.

Witten, I. H., Neal, R. M., and Cleary, J. G., Arithmetic coding for data compression. Commun. ACM 30(6):520–540, 1987.CrossRef

32.

http://www.lomont.org/Software/Misc/ArithComp/ArithComp.html, 2010.

33.

Information technology—Coded representation of picture and audio information—Progressive Bi-level image compression ISO/IEC; International Standard ISO/IEC 11544 and ITU-T Recommendation T.82, 1993.

34.

http://www.openjpeg.org/, 2011.

35.

Obrul, D., Liu, Y. K., Žalik, B., Progressive visualization of losslessly compressed DICOM files over the internet. J. Med. Syst.; in press; available on-line at doi:10.1007/s10916-011-9652-y.

36.

http://www.rarlab.com/, 2010.

Titel: Lossless Compression of Threshold-Segmented Medical Images
verfasst von: Denis Špelič
Borut Žalik
Publikationsdatum: 01.08.2012
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 4/2012
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-011-9702-5

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Lossless Compression of Threshold-Segmented Medical Images

Abstract

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 4/2012

A Reliable User Authentication and Key Agreement Scheme for Web-Based Hospital-Acquired Infection Surveillance Information System

Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

Computerized Disease Profiling Using GPS-Linked Multi-Function Sensor Cartridges

A Non-Repudiated and Traceable Authorization System Based on Electronic Health Insurance Cards

Strong Authentication Scheme for Telecare Medicine Information Systems

Application of Intelligent Systems in Asthma Disease: Designing a Fuzzy Rule-Based System for Evaluating Level of Asthma Exacerbation