Medical image compression is a widely studied field of data processing due to its prevalence in modern digital databases. This domain requires a high color depth of 12 bits per pixel component for accurate analysis by physicians, primarily in the DICOM format. Standard raster-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations. This study proposes a lossless medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing. The algorithm employs fractal pixel traversal coupled with a novel approach of segmentation and meshing between pixel blocks for preprocessing. Furthermore, delta and entropy coding are applied to this concept for a complete compression pipeline. The proposal demonstrates that the data compression achieved via fractal segmentation preprocessing yields enhanced image compression results while remaining lossless in its reconstruction accuracy. CompaCT is evaluated in its compression ratios on 3954 high-color CT scans against the efficiency of industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its reconstruction performance is assessed with error metrics to verify lossless image recovery after decompression. The results demonstrate that CompaCT can compress and losslessly reconstruct medical images, being 37% more space-efficient than industry-standard compression systems.

翻译：医学图像压缩因其在现代数字数据库中的普遍性而成为广泛研究的数据处理领域。该领域需要每个像素分量12位的高色彩深度以支持医生进行精确分析，主要采用DICOM格式。基于标准栅格的图像滤波压缩方法已广为人知，但由于其非专业化实现，在医学领域仍非最优方案。本研究提出一种无损医学图像压缩算法CompaCT，旨在针对像素聚集的空间特征与模式进行动态增强的数据处理。该算法采用分形像素遍历，并结合像素块间分割与网格化的创新方法进行预处理。进一步将差分编码与熵编码应用于此概念，形成完整的压缩流程。研究证明，通过分形分割预处理实现的数据压缩能在保持重建精度无损的同时，获得更优的图像压缩效果。本研究在3954幅高色彩CT扫描图像上评估CompaCT的压缩率，并与行业标准压缩技术（如JPEG2000、RLE、ZIP、PNG）的效率进行对比。通过误差指标评估其重建性能，验证解压后图像的无损恢复能力。结果表明，CompaCT能够实现医学图像的无损压缩与重建，其空间效率较行业标准压缩系统提升37%。