Pardo Arturo, Gutiérrez-Gutiérrez José A, López-Higuera José M, Conde Olga M
Grupo Ingeniería Fotónica, dept. TEISA, Universidad de Cantabria, Avda. Los Castros S/N, 39005 Santander, Cantabria, Spain.
Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Cantabria, Spain.
Biomed Opt Express. 2019 Dec 9;11(1):133-148. doi: 10.1364/BOE.11.000133. eCollection 2020 Jan 1.
Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed , or ACE for short, achieves multi- and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.
许多用于生物医学成像中高光谱测量颜色增强的知名算法都基于统计假设，这些假设会因给定图像中出现的不同像素比例而有很大差异，因此可能会阻碍它们在手术环境中的应用。本文试图解释基于奇异值分解（SVD）的增强方法为何会出现这种情况，并提出将光谱增强与分析分离。由此产生的方法，简称为 ，或简称为ACE，基于当前的光谱亲和度指标实现多光谱和高光谱图像的着色与对比度调整，这些指标能够将光谱数据与特定生物标志物建立物理联系。这产生了可调节的实时结果，类似于当前的最先进算法，同时又不会受到其任何固有的上下文相关限制。作为应用示例展示了该方法的两个应用：静脉对比度增强和高精度发色团浓度估计。
