Automatic gridding of noisy microarray images based on coefficient of variation

Abstract

Microarrays are an influential, maximum throughput tool which has motivated researchers to investigate thousands of genes during the last decade. The large quantity of information presented by microarray images necessitates implementation of automated techniques for proficient processing of microarray images, in order to draw an improved biological conclusion. The majority of the techniques discussed in the literature demand manual intervention, which unavoidably reduces the efficiency and reproducibility of the automation process. The presented novel approach mainly focuses on gridding of noisy microarray images automatically. The projection profiles of the binarized image are obtained. The unduly nonuniform distance between grid lines in noisy microarray images are corrected using the coefficient of variation (CV) of successive differences in the gridding locations. Noisy microarray images from various databases were acquired and tested with the presented approach.