提出了一种基于剪切波的医学图像感兴趣区域(ROI)近无损压缩算法, 用于解决因小波对高维数据表示的局限性使其重构图像与原图像平均结构相似度(MSSIM)较低的问题。首先, 指定感兴趣区域并把其余部分视为背景区域(BG); 对两个区域分别进行剪切波变换, 并选取出能够近似逼近原区域的重要系数进行去噪和初步压缩。然后, 对ROI区域所选取的重要系数进行无损Huffman编码, 对BG区域所选取的重要系数量化并进行Huffman编码实现压缩。最后, 通过Huffman解码和剪切波逆变换实现解压从而获得重构图像。实验结果表明, 与改进多级树集合分裂算法(SPIHT)相比, 在相同压缩比下, 提出的算法所获取的ROI重构图像与原图像ROI的MSSIM提高了4%, 峰值信噪比 (PSNR)是改进SPIHT算法的2.35倍; 而整幅重构图像与原图像的MSSIM提高了3%, PSNR提高了28%。该算法可实现ROI 和BG 的相对质量可调, 适用于图像存档和通信系统(PACS)中的医学图像压缩。

A near lossless Region of Interest(ROI)compression algorithm based on the shearlet transform was proposed for medical images to improve the Mean Structural SIMilarity(MSSIM) between the original image and the reconstructed image. Firstly, the ROI was designated in a medical image and the rests were regard as the Background (BG). Then, the ROI and BG were transformed into shearlet domains respectively, and the significant coefficients which could approximate the original region accurately were selected to be denoised and compressed. Furthermore, the main coefficients in ROI were coded by lossless Huffman coding and those in BG were quantized and coded by Huffman coding. Finally, the reconstructed image was obtained by Huffman decoding and inverse shearlet transform. Experiment results show that the MSSIM and Peak Signal Noise Ratio (PSNR) between the original ROI and the reconstructed image ROI obtained by the new algorithm have increased by 4 percent and 135 percent respectively as compared to the modified Set Partitioning in Hierarchical Trees (SPIHT) algorithm with the same compression ratio. Moreover, for the whole image, the MSSIM and PSNR have increased by 3 percent and 28 percent, respectively. With configurable ROI’s and BG’s quality, the proposed algorithm is suitable for the medical image compression in the Picture Archiving and Communication System(PACS).