Spot Detection and Image Segmentation in DNA??Microarray Data

Abstract

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

A New Method to Grid Noisy cDNA Microarray Images Utilizing Denoising Techniques

“…To perform this method, a binary image is obtained by applying canny edge detection technique [16,17]. Then, the detected spots are filled through performing region filling operation.…”

Impacts of DNA Microarray Technology in Gene Therapy

“…When almost all of the spots on the microarray are circular, the ACS has been proved to be the best approach, whereas ASS finds the shape which best fits the spot when the spots are not circular. The HS method considers pixel intensities in and around each spot to decide whether that specific pixel belongs to that spot (Ahmed et al, 2004;Bengtsson & Bengtsson, 2006;Lehmussola et al, 2006;Qin et al, 2005). After separation of the signal from the background, it comes to intensity extraction, after which the intensity ratios are calculated.…”

‘Extremotaxis’: Computing with a bacterial-inspired algorithm

“…One the major difficulties of microarray technology relates to the processing of large and, importantly, error-loaded images of the dots on the chip surface (Qin et al, 2005). Whatever the source of these errors, those obtained in the first stage of data acquisition (segmentation) are passed down to the subsequent processes, with deleterious results.…”