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首页> 外文期刊>Scientific reports. >Root Mean Square Minimum Distance as a Quality Metric for Stochastic Optical Localization Nanoscopy Images
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Root Mean Square Minimum Distance as a Quality Metric for Stochastic Optical Localization Nanoscopy Images

机译：根均线最小距离作为随机光学定位纳米术图像的质量指标

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A localization algorithm in stochastic optical localization nanoscopy plays an important role in obtaining a high-quality image. A universal and objective metric is crucial and necessary to evaluate qualities of nanoscopy images and performances of localization algorithms. In this paper, we propose root mean square minimum distance (RMSMD) as a quality metric for localization nanoscopy images. RMSMD measures an average, local, and mutual fitness between two sets of points. Its properties common to a distance metric as well as unique to itself are presented. The ambiguity, discontinuity, and inappropriateness of the metrics of accuracy, precision, recall, and Jaccard index, which are currently used in the literature, are analyzed. A numerical example demonstrates the advantages of RMSMD over the four existing metrics that fail to distinguish qualities of different nanoscopy images in certain conditions. The unbiased Gaussian estimator that achieves the Fisher information and Cramer-Rao lower bound (CRLB) of a single data frame is proposed to benchmark the quality of localization nanoscopy images and the performance of localization algorithms. The information-achieving estimator is simulated in an example and the result demonstrates the superior sensitivity of RMSMD over the other four metrics. As a universal and objective metric, RMSMD can be broadly employed in various applications to measure the mutual fitness of two sets of points.
机译：随机光学定位纳米镜中的定位算法在获得高质量图像方面发挥着重要作用。普遍和客观度量是至关重要的，并且需要评估纳米镜图像的质量和定位算法的性能。在本文中，我们提出了根均方最小距离（RMSMD）作为定位纳米型图像的质量指标。 RMSMD在两组点之间的平均值，本地和相互适合度。展示了距离度量的属性以及自身独特的属性。分析了专利的歧义，不连续性和不连续性的准确性，精确，召回和jaccard指标，目前用于文献中的jaccard指数。数值示例演示了RMSMD在四个现有度量上的优点，该度量不能在某些条件下区分不同纳米镜图像的品质。提出了实现单个数据帧的Fisher信息和CRLAMER-RAO下限（CRLB）的无偏见的高斯估计器，以基于定位纳米镜图像的质量和定位算法的性能。在一个例子中模拟了信息实现估计器，结果展示了RMSMD在其他四个度量上的卓越敏感性。作为普遍和客观的公制，RMSMD可以广泛用于各种应用中，以测量两组点的相互适应性。

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