Medical Images Compression for Region of Interest Based on Curvelet Transform and SPIHT Algorithm / 中国医学影像学杂志
Chinese Journal of Medical Imaging
; (12): 786-792, 2014.
Article
en Zh
| WPRIM
| ID: wpr-458073
Biblioteca responsable:
WPRO
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.
Texto completo:
1
Base de datos:
WPRIM
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Revista:
Chinese Journal of Medical Imaging
Año:
2014
Tipo del documento:
Article