Correlation Statistics for cDNA Microarray Image Analysis

cDNA Microarray Data Normalization

A Genetic Approach to cDNA Microarray Image Analysis

Microarray image analysis is a significant tool for cDNA microarrays and it is divided in two main stages: Gridding and Spot-Segmentation. Most of the available microarray image analysis tools require human intervention to specify certain landmarks on the grid, or even to precisely locate individual spots. This paper focuses on the development of an original, fully automated gridding and spot-s...

cDNA microarray image segmentation using root signals

A vector processing based framework suitable for cDNA microarray image segmentation is introduced and analyzed in this paper. By using nonlinear, generalized selection vector filters the framework proposed here classifies the cDNA image data as either microarray spots or image background. The solution converges to a root signal that represents the segmented cDNA microarray image with the regula...

Grow-Cut Based Automatic cDNA Microarray Image

Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images. Images suffer from noise, artifacts, and uneven background, while spots depicted on ima...

Spotted cDNA microarray image segmentation using ACWE

The segmentation of the picture (and, implicitly, the segmentation method used) is one of the three important steps in microarray image processing, together with spot gridding and information extraction. It directly affects the accuracy of gene expression analysis in the data mining process that follows. In 2001 a segmentation method for arbitrary pictures using the Active Contours Without Edge...