A Systematic Examination of Microarray Segmentation Algorithms
Karthik S. A.1, Manjunath S. S.2, Shrinivasa G.3, Sneha C. R.4
1Karthik S. A., Department of ISE, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
2Manjunath S. S., Department of Computer Science and Engineering, ATME, Mysore (Karnataka), India.
3Shrinivasa G., Department of Computer Science and Engineering, ATME, Mysore (Karnataka), India.
4Sneha C. R., Department of Computer Science and Engineering, ATME, Mysore (Karnataka), India.
Manuscript received on 08 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 31 December 2019 | PP: 633-637 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11001292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1100.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Microarray is a significant tool and influential method which is used to analyze the cDNA expression in living beings. With the help of this technology one can compute gene expression profile in massive and parallel way. Microarray image segmentation offers an input for subsequent analysis of the extracted microarray data. This work addresses on the different approaches used for segmentation of microarray images. Based on the morphology, topology of spots various methods such as circular shaped, region based, active-contour model based segmentation, shape based, supervised learning and watershed segmentation has been taken for this study. This paper explores and compiles various non statistical approaches used in the field of microarray image segmentation. Finally general tendencies in microarray image segmentation are presented.
Keywords: Microarray, Mean Absolute Error, Spots, Supervised Learning.
Scope of the Article: Web Algorithms