Medical Images Compression for Region of Interest Based on Curvelet Transform and SPIHT Algorithm
10.3969/j.issn.1005-5185.2014.10.018
- VernacularTitle:基于Curvelet变换和SPIHT算法的医学图像感兴趣区压缩
- Author:
Xiumei CHEN
;
Wei WANG
;
Min TANG
- Publication Type:Journal Article
- Keywords:
Data compression;
Images compression for region of interest;
Image coding;
Algorithms;
Image processing,computer-assisted;
Curvelet transform;
SPIHT algorithm
- From:
Chinese Journal of Medical Imaging
2014;(10):786-792
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To propose a novel compression method for region of interest (ROI) based on Curvelet transform and SPIHT algorithm. Materials and Methods The ROI was firstly extracted without compression, and Curvelet transform was applied for the background regions. The Curvelet coefifcients were coded using SPIHT algorithm. Then the images after compression are obtained by inverse Curvelet transform. The ROI and the background were ifnally overlapped to get the full compressed image. Effect of ROI compression and overall compression were compared, as well as the Curvelet transform and wavelet transform, based on peak signal noise ratio. Results The ROI compression highlighted the region of interest and the visual effect was superior to the overall compression. The peak signal to noise of Curvelet transform was higher than that of wavelet transforms, and the compressed images were more clear for the same proportion. Conclusion ROI compression based on Curvelet transform and SPIHT algorithm can achieve efficient compression images without losing important diagnostic information, which complies with the requirement of high precision and high quality of medical image compression.
