用于PACS的乳房x光图像JPEG量化矩阵优化

D. Campbell, A. Maeder, F. Tapia-Vergara
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引用次数: 2

摘要

临床程序和法律要求越来越多地要求数字化灰度医学图像的JPEG压缩性能提高,同时不损失视觉保真度或不超过有界误差度量。当前JPEG常用的做法是使用默认的通用亮度量化矩阵。作为对定义更适合不同医学图像模式的量化矩阵的初步调查,一个新的候选矩阵已被导出用于乳房x光检查。对于给定的信噪比,新的量化矩阵在质量因子为95%时实现了大约22%的相对压缩比性能改进,在质量因子为85%时实现了15%的相对压缩比性能改进。本研究为一种计算智能方法铺平了道路,该方法可以在批处理模式和自适应的基础上对每个医学图像模式优化量化矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mammogram JPEG quantisation matrix optimisation for PACS
Clinical procedures and legal requirements increasingly demand greater performance in JPEG compression of digitised grayscale medical images without loss of visual fidelity or without exceeding a bounded error metric. Current JPEG common practice uses a default general-purpose luminance quantisation matrix. As an initial investigation into defining more suitable quantisation matrices for different medical image modalities, a new candidate matrix has been derived for mammograms. For a given SNR, the new quantisation matrix achieves a relative compression ratio performance improvement of approximately 22% for a quality factor of 95% and 15% for a quality factor of 85%. This study paves the way for a computationally intelligent approach to optimising the quantisation matrix for each medical image modality in both batch mode and adaptively on an image-by-image basis.
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