Microarray sub-grid detection: A novel algorithm

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis LtdA novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing

Fully Automatic Grid Fitting for Genetic Spot Array Images Containing Guide Spots

In the domain of biotechnology array-based methods are used to gain rapid access to genetic information based on the signals of the individual array elements (spots). For an automated analysis of the spots it is necessary to fit a grid to the spots in the digital image in order to represent the array distortions that may occur in the course of the experiment. In order to make the grid fitting problem tractable in a certain class of experiments spot arrays contain a sub-grid of guide spots with a known signal characteristic. We present an automatic grid fitting method for spot array images containing guide spots. Our approach uses simple image processing methods and takes into account prior knowledge inherent in the imaging process