摘要翻译：
在随机光学定位纳米技术中，定位算法对于获得高质量的图像具有重要的作用。一个通用的、客观的度量是评价纳米图像质量和定位算法性能的关键和必要的。本文提出了均方根最小距离(RMSMD)作为纳米图像定位的质量度量。RMSMD度量两组点之间的平均、局部和相互适应度。给出了距离度量所共有的性质和距离度量所特有的性质。分析了目前文献中使用的精确度、查准率、查全率和Jaccard指数等指标的模糊性、不连续性和不适当性。一个数值例子说明了RMSMD在某些条件下优于现有的四个不能区分不同纳米图像质量的度量。提出了一种基于Fisher信息和Cramer-Rao下限(CRLB)的无偏高斯估计器，用于衡量定位图像的质量和定位算法的性能。仿真结果表明，RMSMD比其它四个指标具有更高的灵敏度。RMSMD作为一种通用的客观度量，可以广泛地应用于测量两个点集的相互适应度。
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英文标题：
《Root Mean Square Minimum Distance as a Quality Metric for Localization
  Nanoscopy Images》
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作者：
Yi Sun
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最新提交年份：
2018
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1801.01876