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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.

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