A shape-independent algorithm for fully-automated gridding of cDNA microarray images. (August 2017)

• Record Type: Journal Article
• Authors: Saberkari, Hamidreza; Shamsi, Mousa; Ghavifekr, Habib Badri
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 135
• Page End: 150
• Publication Date: 2017-08
• Subjects: Microarray -- Gridding -- Otsu thresholding -- De-noising -- Image processing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.06.018
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Image contrast of the microarrays are improved using the Otsu thresholding-based algorithm. A new shape-independent algorithm is proposed for noise elimination of cDNA microarrays. One of the main achievements of the proposed algorithm is its higher gridding accuracy in comparison with other state-of-the-art methods. The stability of the proposed algorithm against some restrictions in microarrays such as noise, artifacts, and irregularities regarding the shapes of spots is high. A user-friendly software package has been designed for gridding the microarrays. Abstract: In this paper, a fully-automated microarray gridding algorithm is presented. This algorithm contains the block finding in an image by using variable length Blackman window, image contrast enhancement based on the Otsu thresholding approach, and identification of image objects including spots and artifacts through the 8-connected labeling method. Furthermore, a shape-independent algorithm based on the area of pixels in each object is proposed for noise elimination. The final gridding is performed using a new method based on the constructed spot matrix and a refinement procedure is exploited to minimize all probable grid-line errors. The performance of the proposed algorithm is evaluated by five public datasets including the Swiss Institute of Bioinformatics (SIB), Joe DeRisi's individual tiff files (DeRisi), University of California, San Francisco (UCSF), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD). The obtained results reveal that the proposed algorithm reaches a higher level of accuracy and stability against some restrictions in microarray images such as noise, artifacts, and irregularities regarding the shapes of spots in comparison with other state-of-the-art methods.