CFP last date
20 May 2024
Reseach Article

Review of Various Techniques for Medical Image Compression

by Harpreet Kaur, Rupinder Kaur, Navdeep Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 4
Year of Publication: 2015
Authors: Harpreet Kaur, Rupinder Kaur, Navdeep Kumar
10.5120/ijca2015905282

Harpreet Kaur, Rupinder Kaur, Navdeep Kumar . Review of Various Techniques for Medical Image Compression. International Journal of Computer Applications. 123, 4 ( August 2015), 25-29. DOI=10.5120/ijca2015905282

@article{ 10.5120/ijca2015905282,
author = { Harpreet Kaur, Rupinder Kaur, Navdeep Kumar },
title = { Review of Various Techniques for Medical Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 4 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number4/21949-2015905282/ },
doi = { 10.5120/ijca2015905282 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:47.838319+05:30
%A Harpreet Kaur
%A Rupinder Kaur
%A Navdeep Kumar
%T Review of Various Techniques for Medical Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 4
%P 25-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image Compression generally refers to reducing the size of an image for the purpose of minimizing storage space as well as reducing the transfer time when transmitted over the network. Compression is very useful in medical imaging as a large quantity of storage is needed for storing medical images which can further be delivered for diagnosis. An appropriate technique for compression is needed for saving storage capacity as well as network bandwidth. It is also necessary that the valuable information should not be lost after compression of an image. In this paper various techniques of lossless image compression for medical images have been reviewed. The evaluation of performance is based on the parameter compression ratio.

References
  1. Xin Bai , Jesse S. Jin & Dagan Feng, “Segmentation-based multilayer diagnosis lossless medical image compression”, Pan-Sydney Area Workshop on Visual Information Processing, pp. 9-14, 2004.
  2. Ming Yang & Nikolaos Bourbakis, “An overview of lossless digital image compression techniques” IEEE 48th Midwest Symposium Circuits and Systems, pp. 1099-1102, 2005.
  3. Charalampos Doukas & Ilias Maglogiannis, “Region of interest coding techniques for medical image compression”, IEEE Engineering in Medicine and Biology Magazine, Vol. 26, No 5, pp. 29- 35, 2007.
  4. Mohammad Kabir Hossain, Shams M Imam, Khondker Shajadul Hasan & William Perrizo, “A lossless image compression technique using generic peano pattern mask tree”, IEEE 11th International Conference on Computer and Information Technology ICCIT, pp. 317-322, 2008.
  5. Mohamed Y. Eltabakh, Wing-Kai Hon, Rahul Shah, Walid G. Aref & Jeffrey S. Vitter, “The SBC-tree: An index for run-length compressed sequences”, ACM In Proceedings of the 11th International Conference on Extending Database Technology: Advances In Database Technology, pp. 523-534, 2008.
  6. Micha Stabno & Robert Wrembel, “RLH: Bitmap compression technique based on run-length and Huffman encoding”, Information Systems, Vol. 34, No. 4, pp. 400-414, 2009.
  7. Qiusha Min, & Robert J.T. Sadleir, “A hybrid lossless compression scheme for efficient delivery of medical image data over the internet”, IEEE Second International Conference on Computer Modeling and Simulation, Vol. 1, pp. 319-323, 2010.
  8. Jau-Ji Shen & Hsiu-Chuan Huang, “An adaptive image compression method based on vector quantization”, First International Conference on Pervasive Computing Signal Processing and Applications (PCSPA), pp. 377-381, 2010.
  9. Xiwen Zhao & Zhihai He, “Local structure learning and prediction for efficient lossless image compression”, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1286-1289, 2010.
  10. Suresh Yerva, Smita Nair & Krishnan Kutty , “Lossless image compression based on data folding”, IEEE International Conference on Recent Trends in Information Technology (ICRTIT), pp. 999-1004, 2011.
  11. Yi-Fei Tan & Wooi-Nee Tan, “Image compression technique utilizing reference points coding with threshold values”, IEEE International Conference on Audio, Language and Image Processing (ICALIP), pp. 74-77, 2012.
  12. Arif Sameh Arif, Rajasvaran Logeswaran, Sarina Mansor & Hezeru Abdul Karim, “Segmentation and compression of pharynx and esophagus fluoroscopic images”, IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 220-225, 2013.
  13. J. Papitha, G. Merlin Nancy & D. Nedumaran, “Compression techniques on MR image- A comparative study”, IEEE International Conference on Communications and Signal Processing (ICCSP), pp. 367-371, 2013.
  14. Srikanth S. & Sukadev Meher, “Compression efficiency for combining different embedded image compression techniques with Huffman encoding”, IEEE International Conference on Communications and Signal Processing (ICCSP), pp. 816-820, 2013.
  15. Victor Sanchez & Joan Bartrina-Rapesta, “Lossless compression of medical images based on HEVC intra coding”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 6622-6626, 2014.
  16. Atef Masmoudi, Afif Masmoudi& William Puech, “An efficient adaptive arithmetic coding for block-based lossless image compression using mixture models”, IEEE International Conference on Image Processing (ICIP), pp. 5646-5650, 2014.
  17. Krishan Gupta, Mukesh Sharma & Kirti Saneja, “A new lossless KMK technique for image compression”, IEEE International Conference on Optimization Reliabilty and Information Technology (ICROIT), pp. 405-408, 2014.
  18. Arif Sameh Arif, SarinaMansor, Rajasvaran Logeswaran & Hezerul Abdul Karim, “Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images”, Springer Science Business Media New York, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Lossless compression medical images compression ratio