New methodology for microarray spot segmentation and gene expression analysis

Abstract

DNA microarray analysis is the main core in genome mapping. Each microarray image contains millions of information about genes. Microarray analysis is considered one of the most recent and important technologies in exploring the genome. One of the key steps in microarray analysis is to extract gene information from the gene spots, these information represent gene expression levels in the microarray. This paper proposes a new methodology to improve microarray spot analysis based on spot extracted segments. It concentrates on each spot segment area independently rather than analyzing all the spots area together of the microarray image. This paper provides a formal model to enhance the intensity values obtained from gene expression levels of the microarray at any intensity expressed level. It also this paper presents the adaptive threshold techniques to be used for microarray segmentation. The experimental results show that the mean of the gene expression intensity value was 87.77.

Sugeno integral generalization applied to improve adaptive image binarization

“…Also for this kind of problems, Information Theory provides quantization strategies, but at the cost of much greater estimation complexity [15]. In genomic and proteomic analyses, the adaptive thresholding is exploited for the study of differential microarray spot intensities [16,17]. The objects analysed in the images can be static or in motion, multiple or single.…”