Robust segmentation model for unshaped microarray spots using fractal transformation
by M.A. SayedElahl; R.M. Farouk
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 17, No. 3, 2022

Abstract: DNA microarray technology has permitted the analysis of global gene expression profiles in clinical diagnosis, treatment, and environmental health research for several diseases including cancer. Microarray images analysis, processing and segmentation are decisive steps in gene expression analysis, since any error leads to improper diagnosis. These techniques increased and enhanced over the past few years. However, the segmentation of the unshaped spots within the microarray image is still a problem due to the variations of spots in shapes and sizes. Hence, we introduce a new unshaped microarray spot segmentation model, which focuses on spots detection and segmentation regardless of their size and shape using fractal dimensions transform. Real microarray images from the Stanford Microarray Images Database (SMD) and Universal Microarray Images Database (UNC) are used to evaluate the efficiency of the proposed model. The numerical and visual results show that the proposed model improves the accuracy and the efficiency of the spots segmentation process. Biological researchers can use this model in their labs and clinics, as a free priceless software instead of using expensive ones like ScanAlyze, GenePix or spot software.

Online publication date: Tue, 27-Sep-2022

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