现代医学成象技术产生了大量的医学数字图象，而这些图象的存储和传输却存在很大问题，传统上，采用无损压缩编码方法改善这些图象的存储和传输效率，全为了达到较高的压缩比，必须采用有损压缩，然而，有损压缩会给图象带来失真，必须谨慎使用，医学图象通常,由二类区域构成，其中一类包含重要的诊断信息，由于其错误描述的代价非常高，因此提供一种高重的质量的压缩方法更加必要，另一类区域的信息较为次要，其压缩的目标则要求达到尽可能高的压缩比，为了既能保证感兴趣区图象的重构质量，又能获得较高压缩比，提出了一种面向任务的医学图象压缩算法，该方法把无损压缩和有损压缩统一在小波变换的框架下，对感兴趣区采用无损压缩，而对其他部分则采用有损压缩，实验证明，该压缩方法在压缩比和重建图象质量上均达到了较好的性能。

Medical imaging techniques produces a lot of digital images. Conventionally, lossless compression methods are used to make store and transmission to be more effective. To achieve high compression ratio, lossy compression must be exploited. However, lossy compression will distorted the original images and so have to be applied carefully. A medical image composes of two categories of regions. The regions in the first category include important information for diagnosis. The compression with very good quality of reconstructed image is necessary to these regions since the cost due to error representation will be high. On the other hand, information in the other category of regions may be less important and so compression to these regions should achieve high ratio. Based on this idea, we proposed a task oriented compression algorithm for medical images in this paper. We integrate the lossless compression and lossy compression via wavelet transform. Lossless compression is applied to the regions of interest and lossy compression to other regions. The proposed algorithm performs well in both compression ratio and quality of reconstructed image.