Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm

• Published in: Journal of medical signals and sensors Vol. 14; no. 2; p. 6
• Main Authors: Bakhshayesh, Nayyer Mostaghim, Shamsi, Mousa, Golabi, Faegheh
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01.02.2024; Wolters Kluwer Medknow Publications
• Subjects: contrast enhancement; genetic algorithm; genomics; gridding; mathematical morphology; microarray images; Original; genetic algorithm; gridding; genomics; mathematical morphology; microarray images; Contrast enhancement
• ISSN: 2228-7477; 2228-7477
• DOI: 10.4103/jmss.jmss_65_22

Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes. In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs. The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively. The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.