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

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.

A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images

“…The highest global gridding accuracy of the SIB dataset reported so far is 89. 29% [27] whereas proposed method gives 98% accuracy. Also, we achieved 2.11%, 1.67% and .03% improvement in local gridding accuracy for SIB, Derisi and GEO datasets than the aforementioned latest work.…”

“…15. The authors of [10], [17] and [27] did gridding on entire images and authors of [14], [19] and [20] worked on cropped subgrids.…”

“…Multilevel thresholds are obtained using physical information from source microarray images. In 2019, the authors introduced a fully automatic gridding technique [27], applying multi-resolution analysis to obtain horizontal and vertical image projection profiles in global and local grid units. Upon identification of the subarray, an adaptive threshold approach is determined to get the grid lines for locations.…”

“…The comparison of our global gridding results with previous works which used the same datasets are displayed in Table 4. The authors in [27] proposed a fully automatic gridding algorithm and had worked on the Derisi dataset. However, they only gave the local gridding results of that dataset.…”