Kang K S, Park H W
Department of Information and Communication Engineering, Korea Advanced Institute of Science and Technology, Seoul.
J Digit Imaging. 1996 Feb;9(1):11-20. doi: 10.1007/BF03168563.
Lossless image coding is important for medical image compression because any information loss or error caused by the image compression process could affect clinical diagnostic decisions. This paper proposes a lossless compression algorithm for application to medical images that have high spatial correlation. The proposed image compression algorithm uses a multi-level decomposition scheme in conjunction with prediction and classification. In this algorithm, an image is divided into four subimages by subsampling. One subimage is used as a reference to predict the other three subimages. The prediction errors of the three subimages are classified into two or three groups by the characteristics of the reference subimage, and the classified prediction errors are encoded by entropy coding with corresponding code words. These subsampling and classified entropy coding procedures are repeated on the reference subimage in each level, and the reference subimage in the last repetition is encoded by conventional differential pulse code modulation and entropy coding. To verify this proposed algorithm, it was applied to several chest radiographs and computed tomography and magnetic resonance images, and the results were compared with those from well-known lossless compression algorithms.
无损图像编码对于医学图像压缩非常重要，因为图像压缩过程中造成的任何信息丢失或错误都可能影响临床诊断决策。本文提出了一种适用于具有高空间相关性的医学图像的无损压缩算法。所提出的图像压缩算法使用了一种结合预测和分类的多级分解方案。在该算法中，通过下采样将一幅图像划分为四个子图像。一个子图像用作参考来预测其他三个子图像。根据参考子图像的特征，将这三个子图像的预测误差分为两组或三组，并且通过使用相应码字的熵编码对分类后的预测误差进行编码。在每一级中，对参考子图像重复这些下采样和分类熵编码过程，并且对最后一次重复中的参考子图像通过传统的差分脉冲编码调制和熵编码进行编码。为了验证所提出的算法，将其应用于若干胸部X光片、计算机断层扫描图像和磁共振图像，并将结果与来自知名无损压缩算法的结果进行比较。
