POLAR MODELLING AND SEGMENTATION OF GENOMIC MICROARRAY SPOTS USING MATHEMATICAL MORPHOLOGY

Authors

  • Jesús Angulo

DOI:

https://doi.org/10.5566/ias.v27.p107-124

Keywords:

genomic microarray image, mathematical morphology, polar coordinates, shortest path segmentation, spot modelling, spot segmentation

Abstract

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.

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Published

2011-05-03

How to Cite

Angulo, J. (2011). POLAR MODELLING AND SEGMENTATION OF GENOMIC MICROARRAY SPOTS USING MATHEMATICAL MORPHOLOGY. Image Analysis and Stereology, 27(2), 107–124. https://doi.org/10.5566/ias.v27.p107-124

Issue

Section

Original Research Paper

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