Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI.
IEEE Trans Med Imaging. 1992;11(3):302-18. doi: 10.1109/42.158934.
The performance of the eigenimage filter is compared with those of several other filters as applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. The conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.
将特征像滤波器的性能与其他几种滤波器在磁共振图像（MRI）场景序列中的图像增强和分割应用进行了比较。比较了主成分分析、匹配、修正匹配、最大对比度、目标点、比、对数比和角度图像滤波器。信噪比（SNR）、对比度噪声比（CNR）、所需特征的分割（SDF）和部分容积平均效应的校正（CPV）被用作性能指标。为了进行比较，推导了滤波后图像的 SNR 和 CNR 的解析表达式，并研究了线性滤波器的 CPV。通过对模拟和获取的磁共振成像序列的应用，说明了滤波器的特性，包括一个体模研究和一个临床病例；讨论了优缺点。结论是特征像滤波器是同时实现 SDF 和 CPV 的最优线性滤波器。
