Polar Modelling And Segmentation Of Genomic Microarray Spots Using Mathematical Morphology
Polar Modelling And Segmentation Of Genomic Microarray Spots Using Mathematical Morphology
复制标题
DOI：
10.5566/ias.v27.p107-124
复制
发表时间：
2011-05
影响因子：
0.9
通讯作者：
J. Angulo
中科院分区：
文献类型：
--
作者：
J. Angulo
微阵列中斑点的稳健图像分析（质量控制+斑点分割+定量）是自动化软件的要求，这对于基于基因组学微阵列数据的高通量分析至关重要。本文论述了基于模型的图像处理算法的发展，用于根据其形态学自适应地对每个斑点进行定性/分割/量化。介绍了一系列光斑强度的形态学模型。斑点类型学代表了从大型数据库中识别出的大多数可能的定性案例（不同的例程、技术等）。然后，基于这些斑点模型，分类框架已经开发。斑点特征提取和分类（没有分割）基于将斑点图像转换为极坐标，并且在计算径向/角度投影之后，计算粒度曲线和从这些投影导出的参数。点轮廓分割也可以通过在极坐标下工作来解决，计算上/下最小路径，这很容易用广义距离函数获得。利用这种基于模型的技术，可以通过控制算法的不同元素来规则化分割。根据斑点类型学（例如，甜甜圈状或蛋状斑点），可以计算几个最小路径以获得多区域分割。此外，这种分割是更强大和敏感的弱点，改善了以前的方法。
Robust image analysis of spots in microarrays (quality control + spot segmentation + quantification) is a requirement for automated software which is of fundamental importance for a high-throughput analysis of genomics microarray-based data. This paper deals with the development of model-based image processing algorithms for qualifying/segmenting/quantifying adaptively each spot according to its morphology. A series of morphologicalmodels for spot intensities are introduced. The spot typologies representmost of the possible qualitative cases identified from a large database (different routines, techniques, etc.). Then, based on these spot models, a classification framework has been developed. The spot feature extraction and classification (without segmenting) is based on converting the spot image to polar coordinates and, after computing the radial/angular projections, the calculation of granulometric curves and derived parameters from these projections. Spot contour segmentation can also be solved by working in polar coordinates, calculating the up/downminimal path, which is easily obtained with the generalized distance function. With this model-based technique, the segmentation can be regularised by controlling different elements of the algorithm. According to the spot typology (e.g., doughnut-like or egg-like spots), several minimal paths can be computed to obtain a multi-region segmentation. Moreover, this segmentation is more robust and sensible to weak spots, improving the previous approaches.