Abstract
This paper mainly focusses on the image gridding and segmentation methods of microarray analysis. The process of gridding is to divide the image into sub-array of spots (sub-gridding) and sub-arrays are again divided into spot areas (spot detection). Most of the existing methods depend on parameters such as number of rows/columns, spots count in each row/column, and size of sub-array. In this paper, a gridding algorithm is presented without any human intervention removing any parameter initializations. In the segmentation step, first the pixels are classified as spot/background using Support Vector Machine (SVM). This classification result is used for segmentation of spot area in gridded image block. The results show the proposed algorithms that perfectly grids the microarray image and perfectly segments the spot area from background. The log-ratio values calculated for each spot determines the transcription abundance of each gene.
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References
Harikiran J et al (2014) Fast clustering algorithms for segmentation of microarray images. IJSER 5(10):569–574
Harikiran J et al (2017) Noise removal in microarray images using variational mode decomposition technique. TELKOMNICA Telecommun Comput Electron Control 15(4):1750–1757
Harikiran J et al (2012) Spot edge detection in microarray images using bi-dimensional empirical mode decomposition. In: Proceedings of C3IT-2012, vol 4, pp 19–25
Shao G et al (2019) Automatic microarray image segmentation with clustering based algorithms. Plus One Res
Chen Y et al (2009) Microarray image analysis and gene expression ratio statistics. In: CSAG, pp 1–19
Farouk RM et al (2019) Microarray spot segmentation algorithm based on integro-differential operator. Egypt Inform J 173–178
Katsigiannis S et al. MIGS-GPU: microarray image gridding and segmentation on the GPU. IEEE JBHI 21(3):867–874
Ni SH et al (2009) Spotted cDNA microarray image segmentation using ACWE. RJIST 12(2):249–263
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Sai Chandana, B., Harikiran, J., Srinivasa Rao, B., Subbareddy, T. (2021). Gridding and Supervised Segmentation Method for DNA Microarray Images. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_8
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DOI: https://doi.org/10.1007/978-981-15-8767-2_8
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