为解决海量医学数据与有限存储空间和传输带宽之间的矛盾,提出一种适用于PACS(picture archiving and communication system)系统的医学图像近无损压缩算法。首先对病变区域和背景区域分别进行剪切波变换和小波变换;其次,选取一些能够近似逼近病变区域图像的重要系数达到去噪和初步压缩的目的;然后,对病变区域所选取的重要系数进行无损Huffman编码,同时对背景区域所得小波系数进行量化和多级树集合分裂算法(SPIHT)编码实现压缩;最后,融合各区域经解码和逆变换得到的图像获得整幅重构图像。实验结果表明,新算法在与小波有损压缩方法设置同样压缩比的情况下,所获取的病变区域重构图像和原病变区域的平均结构相似度(MSSIM)提高了6%,峰值信噪比(PSNR)是小波有损压缩方法的2.54倍,而整幅重构图像与原图像的MSSIM提高了2%,PSNR提高了13%。

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.