Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.
Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.
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DOI：
10.1021/acs.analchem.5b03159
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发表时间：
2015-10-06
影响因子：
7.4
通讯作者：
Haab BB
中科院分区：
文献类型：
--
作者：
Ensink E;Sinha J;Sinha A;Tang H;Calderone HM;Hostetter G;Winter J;Cherba D;Brand RE;Allen PJ;Sempere LF;Haab BB
某些实验涉及图像数据的高通量量化，因此需要自动化算法。开发这种算法的一个挑战是在宽范围的图像特性上正确地解释信号，而不需要手动调整参数。在这里，我们提出了一种新的方法来定位图像数据中的信号，称为分段和拟合拟合保持（SFT）。该方法评估图像的小片段的统计特性，并确定统计数据之间的最佳拟合趋势。基于这些关系，SFT识别属于背景区域的片段;分析背景以确定最佳阈值;并分析所有片段以识别信号像素。我们优化了抗体微阵列和免疫荧光数据中定位背景和信号的初始设置，发现SFT在多个不同的图像特征上表现良好，无需重新调整设置。当用于多色，组织微阵列图像的自动化分析，SFT正确地发现与已知的亚细胞定位标记的重叠，它比一个固定的阈值和大津的方法为选定的图像进行更好。SFT有望推进图像分析全自动化的目标。
Certain experiments involve the high-throughput quantification of image data, thus requiring algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multi-color, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis.