A Fully Automated Spot Detection Approach for cDNA Microarray Images Using Adaptive Thresholds and Multi-Resolution Analysis

Mary Monir Saeid,Mohamed Ali Saleh,Zaki B Nossair

doi:10.1109/access.2019.2923560

Mary Monir Saeid, Mohamed Ali Saleh + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2923560

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Abstract

The problem of gridding microarray images remains a challenging task. This is because microarray images are usually contaminated with noise and artifacts, such as low intensity and poor quality spots. In this paper, a new gridding technique for microarray images is introduced. The proposed technique includes both global gridding (sub-array detection) and local gridding (individual spot detection). Our technique is developed based on multi-resolution analysis and a new adaptive threshold method. The proposed framework is fully automated in the sense that it does not need any user intervention and the only input required is the microarray image. The presented technique can be applied to images with different specifications, such as resolution, number of sub-arrays, number of spots in each sub-array, and noise levels. The experimental results show that the proposed method is highly accurate when compared with the existing software tools as well as with recently published techniques. Our results also show that the presented approach is very effective for gridding microarray images with low intensity, poor quality spots, and missing/irregular spots. The spot detection accuracy of the proposed method is improved by up to 5.48% compared with that of the other published algorithms.

Highlights

Deoxyribonucleic acid (DNA) microarray technology is a powerful tool for evaluating the expression levels for several thousands of genes simultaneously
The approach in [14] describes a global gridding methodology in which Radon transform is used for detecting and correcting rotations and the algorithm applies morphological operators to separate the sub-arrays in a cDNA microarray image
The DNA microarray imaging technology has led to enormous progress in the life sciences by allowing scientists to analyze the expression of thousands of genes at a time

Summary

INTRODUCTION

Deoxyribonucleic acid (DNA) microarray technology is a powerful tool for evaluating the expression levels for several thousands of genes simultaneously. The approach in [12] describes a fully automatic gridding methodology using intensity projection profiles of microarray images This gridding method is sensitive to contaminations and large number of missing spots. The approach in [14] describes a global gridding methodology in which Radon transform is used for detecting and correcting rotations and the algorithm applies morphological operators to separate the sub-arrays in a cDNA microarray image. The proposed adaptive threshold method will be applied in local gridding because the projection profiles of the microarray images are often non-uniform due to some spots may be missed or the spots may have low intensity and poor quality [25], [20].

THE PROPOSED LOCALGRIDDING APPROACH

Findings

CONCLUSION