Abstract
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.
Abstract (translated)
医学图像压缩是数据处理中一个广泛研究的领域，由于其在现代数字数据库中的普遍应用。该领域的图像需要每像素组件12位的颜色深度以实现医生准确分析，主要采用DICOM格式。通过滤波器进行标准光栅图像压缩是一个众所周知的方法；然而，由于非专业实施，在医学领域仍然次优。本研究提出了一种无损的医疗图像压缩算法CompaCT，旨在针对空间特征和像素浓度模式进行动态增强的数据处理。该算法采用了分形像素遍历，并结合了像素块之间的分割与网格化的新方法作为预处理步骤。此外，还应用了差值编码和熵编码的概念以完成完整的压缩流程。提案表明通过分形单元预处理实现的数据压缩可改善图像压缩结果的同时保持重建的无损准确性。CompaCT在3954个高色深CT扫描上的压缩比进行了评估，并与行业标准压缩技术（如JPEG2000、RLE、ZIP和PNG）的效率进行了比较。其重建性能通过误差度量来验证解压后图像是否为无损恢复。结果显示，CompaCT能够对医疗图像进行压缩并实现无损重建，比行业标准压缩系统节省37%的空间。
URL
https://arxiv.org/abs/2308.13097
