Reducing the variability in cDNA microarray image processing by Bayesian inference

Reducing the variability in cDNA microarray image processing by Bayesian inference

MOTIVATION Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of...

cDNA Microarray Data Normalization

FPGA based system for automatic cDNA microarray image processing

Automation is an open subject in DNA microarray image processing, aiming reliable gene expression estimation. The paper presents a novel shock filter based approach for automatic microarray grid alignment. The proposed method brings up significantly reduced computational complexity compared to state of the art approaches, while similar results in terms of accuracy are achieved. Based on this ap...

cDNA microarray image processing using fuzzy vector filtering framework

This paper presents a novel filtering framework capable of processing cDNA microarray images. The proposed two-component adaptive vector filters integrate well-known concepts from the areas of fuzzy set theory, nonlinear filtering, multidimensional scaling and robust order-statistics. By appropriately setting the weighting coefficients in a generalized framework, the method is capable of removi...

cDNA Microarray Genome Image Processing Using Fixed Spot Position

Complementary DNA (cDNA) microarray is one of the most recent and important technology for exploring the genome. cDNA microarray Image analysis aims to measure the intensity for each spot in the scanned image and this intensity represents the amount of a specific gene in the studied cell. This paper presents a new algorithm that achieves an automated way for applying grid in the cDNA microarray...