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基于最优多级阈值的dna微阵列图像子网格和斑点检测

Sub-grid and Spot Detection in DNA Microarray Images using Optimal Multi-level Thresholding
课程网址: http://videolectures.net/prib2010_rueda_ssdm/  
主讲教师: Luis Rueda
开课单位: 温莎大学
开课时间: 2010-10-14
课程语种: 英语
中文简介:
DNA微阵列图像的分析是基因表达分析中的关键步骤,因为早期阶段的任何错误都在分析的未来步骤中传播。当处理底层图像时,准确地分离子网格和斑点对于包括分割,量化,归一化和聚类的后续步骤是极其重要的。我们提出了一种全自动方法,首先在给定整个微阵列图像的情况下检测子网格,然后检测每个子网格中斑点的位置。该方法首先通过仿射变换检测并校正图像中的旋转,然后是多项式时间最优多级阈值算法以找到子网格和点的位置。另外,提出了新的有效性指数,以便在微阵列图像中找到正确数量的子网格,以及每个子网格中正确的点数。对现实生活微阵列图像的广泛实验表明,该方法可以自动且高精度地执行这些任务。
课程简介: The analysis of DNA microarray images is a crucial step in gene expression analysis, since any errors in early stages are propagated in future steps in the analysis. When processing the underlying images, accurately separating the sub-grids and spots is of extreme importance for subsequent steps that include segmentation, quantification, normalization and clustering. We propose a fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach first detects and corrects rotations in the images by an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm to find the positions of the sub-grids and spots. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the microarray image, and the correct number of spots in each sub-grid. Extensive experiments on real-life microarray images show that the method performs these tasks automatically and with a high degree of accuracy.
关 键 词: 微阵列图像; 仿射变换检测; 阈值算法
课程来源: 视频讲座网
最后编审: 2019-09-14:lxf
阅读次数: 31