Comparative Analysis of Lempel-Ziv-Welch and Embedded Zero Tree Compression Algorithms on Kidney Images.

Simple item page
dc.contributor.authorBabatunde Ronke Seyi
dc.date.accessioned2024-07-24T09:32:24Z
dc.date.available2024-07-24T09:32:24Z
dc.date.issued2020-07-24
dc.description.abstractImage compression refers to the reduction of digital image data to store or transmit images with less rate of distortion. It is the reduction of the quantity of graphical data required to represent a digital image. Medical images play an important role in providing detailed information about patient injuries, fractures and other critical issues related to different disease and discomfort. Transmission of a reduced size of image data will help reduce computational burden as well as storage cost. Several compression algorithms have been adopted for use by numerous research on different types images, of which embedded zero tree (EZT) and Lempel-Ziv-Wetch (LZW) compression algorithms are most commonly used amongst others. The choice of a suitable compression algorithm for compression task on specific images has been an open issue, which this research seeks to look into. In this research, the compression capability of embedded zero tree (EZT) and Lempel-Ziv-Wetch (LZW) compression algorithms on Kidney images was compared. The performance of these algorithms was evaluated using peak signal noise ratio (PSNR), and compression ratio (CR). Effective compression implies that the resultant stream of image will be smaller than the original image size, without loss of vital content. Empirical results obtained shows that the LZW, having PSNR and CR of 97.28 and 2.85 respectively outperforms EZT with PSNR of 89.17 and CR of 1.82 when the same sets of images were subjected under the two compression algorithms. The results from this research suggest the usability of LZW for compression task involving kidney images. Similarly, lossless compression is recommended because loss of vital information content of medical images could lead to wrong diagnosis.
dc.identifier.citationBabatunde et. al., 2020
dc.identifier.issnISSN 2645-2960; Print ISSN: 2141-3959
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/1774
dc.language.isoen
dc.publisherPublished by Faculty of Computing and Information Science. University of Ilorin. Special Issue on Computing and Communication Technologies
dc.titleComparative Analysis of Lempel-Ziv-Welch and Embedded Zero Tree Compression Algorithms on Kidney Images.
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
14_LZW_ImageComprssionI_Fagbola_Thani_IJIPC_SP20_pp 199-208.pdf
Size:
861.7 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Scholarly Publication