Volume 18, Issue 4, December 2016, Pages 1023–1031
Mithun Kumar Mondal1, Liton Devnath2, Malati Mazumder3, and Md. Rafiqul Islam4
1 Mathematics Discipline, SET School, Khulna University, Bangladesh
2 Mathematics Discipline, SET School, Khulna University, Bangladesh
3 Applied Mathematics, Gono Bishwabidyalay, Bangladesh
4 Mathematics Discipline, Science Engineering and Technology School, Khulna University, Khulna-9208, Bangladesh
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This study addresses some mathematical and statistical techniques of medical image compression and their computational implementation. Fundamental theories have been presented, applied and illustrated with examples. To make the report as self-contained as possible, key terminologies have been defined and some classical results and theorems are stated, in the most part, without proof. Some algorithms and techniques of image processing have been described and substantiated with experimentation using MATLAB. Medical image compression is necessary for huge database storage in Medical Centers and medical data transfer for the purpose of diagnosis. Wavelet transforms present one such approach for the purpose of compression. The same has been explored in study with respect to wide variety of medical images. In this approach, the redundancy of the medical image and DWT coefficients are reduced through thresholding and further through Huffman encoding. In this study our main goal is to compare different types of wavelets for medical image compression. Finally, implementation of the above-mentioned concepts is illustrated.
Author Keywords: Wavelet Transform, DWT, Thresholding, Huffman Encoding, Lossy Compression, MSE, PSNR, BPP, CR.
Mithun Kumar Mondal1, Liton Devnath2, Malati Mazumder3, and Md. Rafiqul Islam4
1 Mathematics Discipline, SET School, Khulna University, Bangladesh
2 Mathematics Discipline, SET School, Khulna University, Bangladesh
3 Applied Mathematics, Gono Bishwabidyalay, Bangladesh
4 Mathematics Discipline, Science Engineering and Technology School, Khulna University, Khulna-9208, Bangladesh
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study addresses some mathematical and statistical techniques of medical image compression and their computational implementation. Fundamental theories have been presented, applied and illustrated with examples. To make the report as self-contained as possible, key terminologies have been defined and some classical results and theorems are stated, in the most part, without proof. Some algorithms and techniques of image processing have been described and substantiated with experimentation using MATLAB. Medical image compression is necessary for huge database storage in Medical Centers and medical data transfer for the purpose of diagnosis. Wavelet transforms present one such approach for the purpose of compression. The same has been explored in study with respect to wide variety of medical images. In this approach, the redundancy of the medical image and DWT coefficients are reduced through thresholding and further through Huffman encoding. In this study our main goal is to compare different types of wavelets for medical image compression. Finally, implementation of the above-mentioned concepts is illustrated.
Author Keywords: Wavelet Transform, DWT, Thresholding, Huffman Encoding, Lossy Compression, MSE, PSNR, BPP, CR.
How to Cite this Article
Mithun Kumar Mondal, Liton Devnath, Malati Mazumder, and Md. Rafiqul Islam, “Comparison of Wavelets for Medical Image Compression Using MATLAB,” International Journal of Innovation and Applied Studies, vol. 18, no. 4, pp. 1023–1031, December 2016.
