Abstract: Wavelets two-dimension are good at isolating the discontinuities at edge points,but not the smoothness along the contours．In addition,separable wavelets only capture limited directional information,which is restricted in image processing applications．In comparison,contourlet transform combines Laplacian pyramid (LP) with directional filter bank (DFB) to achieve a flexible multi-resolution,local and directional image expansion based on contour segments．A novel image compression method based on contourlet transform and set partitioning in hierarchical trees (SPIHT) algorithm was proposed for colorful medical images,because SPIHT algorithm based on wavelet transform can’t express the texture and contour effectively．Firstly,original RGB image is converted to YIQ color space according to the characteristics of human visual system．Secondly,contourlet transform is applied to region of interest (ROI) to capture the main characteristics and then SPIHT algorithm is used to guarantee the compressed image quality and detail．For back ground image,wavelet transform is used to improve compression ratio greatly by wavelet coefficients truncation．Experimental results demonstrate that our algorithm is practical and effective for colorful medical images,which is a good balance for compressed image quality and compression ratio.

Key words: Contourlet transform,SPIHT,Image compression,Region of interest (ROI),Medical images