To solve the contradiction between the enormous amount of medical image data and the limited storage space and transfer bandwidth

we propose a near-lossless compression algorithm for medical images in PACS(picture archiving and communication system). First

the lesion area and background are transformed into the shearlet domain and wavelet domain respectivel. Second

we select the significant coefficients that can approximate the lesion area accurately for denoising and preliminary compression. Third

we make lossless Huffman coding to the significant coefficients selected by the previous step and process the wavelet coefficients of the background by quantization and SPIHT coding. Finally

we use two result images processed by decoding and inverse transform and obtain the complete reconstructed image. Experiment results show that the MSSIM and PSNR between the original lesion area and the reconstructed image obtained by the new algorithm increased by 6% and 154% respectively compared to the wavelet-based method with the same compression ratio

for the whole image

the MSSIM and PSNR increased by 2% and 13% respectively.