Sub-grid and Spot Detection in DNA Microarray Images Using Optimal Multi-level Thresholding

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.