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Reseach Article

An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images

by Trupti Baraskar, Vijay R. Mankar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 2
Year of Publication: 2017
Authors: Trupti Baraskar, Vijay R. Mankar
10.5120/ijca2017913916

Trupti Baraskar, Vijay R. Mankar . An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images. International Journal of Computer Applications. 166, 2 ( May 2017), 6-10. DOI=10.5120/ijca2017913916

@article{ 10.5120/ijca2017913916,
author = { Trupti Baraskar, Vijay R. Mankar },
title = { An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 2 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number2/27639-2017913916/ },
doi = { 10.5120/ijca2017913916 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:35.080622+05:30
%A Trupti Baraskar
%A Vijay R. Mankar
%T An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 2
%P 6-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today more and more digital medical images are used by physicians in their clinical diagnosis. DICOM format images are used by physicians in their clinical diagnosis. DICOM images are too large in size. They require much space for storage and bandwidth for transmission. Thus, medical images are to be compressed due to their large size and repeated usage for diagnostic purpose. Three pattern Huffman compression algorithm uses the concept of pattern creation. The operation of pattern creation is to create patterns using fixed length coding. Using these patterns, compression of image is carried out. Three best patterns are created in such a way, which yields better compression ratios. Three pattern Huffman compression algorithm uses lossless compression technique and can be applied to all types of medical images like CT scans, MRIs, PET, etc. without compromise in quality. The core concept of the algorithm is based on building up a collection of n-length patterns in the image. The basic model of new compression algorithm is similar to that of the Huffman encoder except for the pattern finder. The operation of the pattern finder is to find the best pattern, which is the most frequent occurring pattern. Therefore the best pattern will also be an input to the encoder. The output of the encoder will be the code along with footer information.

References
  1. J. Janet and T.R. Netesan, “effective compression algorithm for Medical Images as an Aid to Telemedicine”, Asian Journal of Information Technology 4(12); 1180 – 1186, 2005.
  2. Neil F. Johnson, “In Search of the Right Image: Recognition and Tracking of Images in Image Databases, Collections, and the Internet.” Center for Secure Information Systems, George Mason University, 1999
  3. E.R. Fiala and D.H. Greene, “Data compression with finite windows,”Comm. of the ACM, vol. 32, pp. 490–505, April 1989.
  4. R. G. Gallager, “Variations on a theme by Huffman,” IEEE trans. Inf. Theory, vol. 24, pp. 668–674, Nov. 1978.
  5. M. Buro, “On the maximum length of Huffman codes,” Information Processing Letters, vol. 45, pp. 219–223, 1993.
  6. Ida Mengyi Pu, Fundamental Data Compression, 1st ed., Butterworth-Heinemann publications, 2006.
  7. Jagadish H Pujar, “A New Lossless Method of Image Compression And Decompression Using Huffman Coding Techniques”, Journal of Theoretical and Applied Information Technology, vol. 15, no. 1, pp. 18-23, 2010.
  8. Nelson, M. and J.L. Gailly, 1995, “The Data Compression Book, 2nd Edition, M and T Book, New York.
  9. Li-yangchun, “Image Compression Based on P-tree”, in 2nd International Conference Information Science and Engineering (ICISE), pp. 4550-4553, Dec. 4-6, 2010.
  10. Jeeffrey Scott Vitter, "Dynamic Huffman Coding", ACM Transactions on Mathematical Software, vol. 15, no. 2, pp. 158-167, June 1989.
  11. C. Saravanan & R. Ponalagusamy, “Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding”, International Journal of Image Processing (IJIP), Volume (3) : Issue (5)
  12. Mridul Kumar Mathur, Seema Loonker, Dr. Dheeraj Saxena, “Lossless Huffman Coding Technique for Image Compression and Reconstruction using B trees, IJCTA, Jan-Feb 2012
  13. D Mohandas, "A Novel Huffman Coding Mechanism For Medical Image Compression In Telemedicine", Chapter3, shodhganga.in_bnet.ac.in, 2013
  14. Anuj Bhardwaj and Rashid Ali “Image Compression Using Modified Fast Haar Wavelet Transform”, World Applied Sciences Journal 7 (5): 647-653, 2009, ISSN 1818-4952 © IDOSI Publications, 2009
  15. J. Janet and T.R. Netesan, “effective compression algorithm for Medical Images as an Aid to Telemedicine”, Asian Journal of Information Technology 4(12); 1180 – 1186, 2005.
  16. D Mohandas, "A Novel Huffman Coding Mechanism For Medical Image Compression In Telemedicine", Chapter3, shodhganga.in_bnet.ac.in, 2013
Index Terms

Computer Science
Information Sciences

Keywords

DICOM Three Pattern Compression Pattern Finder Haar Transform