UTHM Institutional Repository

Enhancement of medical Image Compression by using Threshold Predicting Wavelet-Based Algorithm

Taujuddin, Nik Shahidah Afifi and Ibrahim, Rosziati (2015) Enhancement of medical Image Compression by using Threshold Predicting Wavelet-Based Algorithm. In: Advanced Computer and Communication Engineering Technology. Springer International Publishing, Switzerland, pp. 755-765. ISBN 978-3-319-07673-7

[img]
Preview
PDF
ICOCOE_shahidah_v2.pdf - Submitted Version

Download (307kB)

Abstract

In recent decades with the rapid development in biomedical engineering, digital medical images have been becoming increasingly important in hospitals and clini-cal environment. Apparently, traversing medical images between hospitals need a complicated process. Many techniques have been developed to resolve these problems. Compressing an image will reduces the amount of redundant data with the good quality of the reproduced image sufficiently high, depending on the ap-plication. In the case of medical images, it is important to reproduce the image close to the original image so that even the smallest details readable. The aim of this paper is to reveal our new proposed compression algorithm. It started by segmenting the image area into Region of Interest (ROI) and Region of Back-ground (ROB) and use the special features provide by wavelet algorithm to pro-duce efficient coefficients. These coefficients is then will be thresholded by using our new proposed thresholding predicting algorithm. This still under-going pro-ject is expected to produce a fast compression algorithm besides decreasing the image size without tolerating with the precision of image quality.

Item Type: Book Section
Subjects: T Technology > T58.5-58.64 Information technology
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Mrs. Nik Shahidah Afifi Md Taujuddin
Date Deposited: 13 Aug 2018 03:38
Last Modified: 13 Aug 2018 03:38
URI: http://eprints.uthm.edu.my/id/eprint/8596
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year