CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique

by Aliaa Saad El-gawady, Mohamed Meselhy Eltoukhy, Ghada El-tawel, M.e. Wahed
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 12
Year of Publication: 2015
Authors: Aliaa Saad El-gawady, Mohamed Meselhy Eltoukhy, Ghada El-tawel, M.e. Wahed
10.5120/19370-1059

Aliaa Saad El-gawady, Mohamed Meselhy Eltoukhy, Ghada El-tawel, M.e. Wahed . Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique. International Journal of Computer Applications. 110, 12 ( January 2015), 30-34. DOI=10.5120/19370-1059

@article{ 10.5120/19370-1059,
author = { Aliaa Saad El-gawady, Mohamed Meselhy Eltoukhy, Ghada El-tawel, M.e. Wahed },
title = { Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 12 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number12/19370-1059/ },
doi = { 10.5120/19370-1059 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:11.748611+05:30
%A Aliaa Saad El-gawady
%A Mohamed Meselhy Eltoukhy
%A Ghada El-tawel
%A M.e. Wahed
%T Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 12
%P 30-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

DNA microarray image processing becomes a viable branch of bioinformatics, its importance stems from the fact that it allows viewing and measuring tens of thousands of genes concurrently. Many techniques were introduced to develop and improve the mission of processing DNA microarray images. The aim of this study is to make a segmentation of the cDNA microarray images. The Marker Controlled Watershed technique is used to segment the DNA microarray spots. The proposed method starts with preprocessing step; i. e. denoising and histogram equalization. Then, the spots are segmented from its background. The used images in this paper were obtained from Stanford Microarray Database (SMD). The obtained results of the developed method are compared to the results of K-means clustering method and fuzzy c-means clustering method. We can conclude that the Marker Controlled Watershed technique is efficient for segmenting the cDNA microarray images.

References
  1. Sarhan A. M. 2010. A novel gene-based cancer diagnosis with wavelets and support vector machines. European Journal of Sci- enti?c Research. 46(4), 488-502.
  2. Athanasiadis E. , Cavouras D. , Kostopoulos S. , Glotsos D. , Kalatzis I. , Nikiforidis G. 2011. A wavelet-based markov ran- dom ?eld segmentation model in segmenting microarray ex- periments. Computer Methods and Programs in Biomedicine. 104(3), 307-315.
  3. Sarhan A. M. 2009. Cancer classi?cation based on microarray gene expression data using DCT and ANN. Journal of Theo- retical and Applied Information Technology (JATIT). 6(2), 208- 216.
  4. Manjunath S. S. , Shreenidhi B. S. , Nagaraja J. , Pradeep B. S. 2013. Morphological spot detection and analysis for microarray images. International Journal of Innovative Technology and Ex- ploring Engineering (IJITEE), New Delhi, India. 2(5), 189-193.
  5. Giannakeas N. , Karvelis P. S. and Fotiadis D. I. 2008. A classi?cation-based segmentation of cDNA microarray im- ages using support vector machine. 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada. (2008), 875-878.
  6. Deepa J. , Thomas T. 2009. Automatic gridding of DNA microarray images using optimum subimage. International Journal of Recent Trends in Engineering. 1(4), 37-40.
  7. Athanasiadis E. I. , Cavouras D. A. , Spyridonos P. P. , Glotsos D. Th. , Kalatzis I. K. , Nikiforidis G. C. 2009. Complementary DNA microarray image processing based on the Fuzzy Gaussian mixture model. IEEE Transaction on Information Technology in Biomedicine. 13(4), 419-425.
  8. Park C. , Lee K. and Lee S. 2004. Automatic Microarray Image Segmentation Based on Watershed Transformation. 17th Inter- national Conference on Pattern Recognition (ICPR'04), Cam- bridge, UK. (Aug. 23-26 2004), 786-789.
  9. Demirkaya O. , Asyali M. H. , Shoukri M. M. 2005. Segmentation of cDNA microarray spots using Markov random ?eld modeling. Bioinformtics. 21(13), 2994-3000.
  10. Yang Y. H. , Buckley M. J. , Dudoit S. , Speed T. P. 2002. Comparison of methods for image analysis on cDNA microarray data. Journal of Computational Graphical Statistics. 11, 108- 136.
  11. Athanasiadis E. I. , Cavouras D. A. , Glotsos D. Th. , Georgiadis P. V. , Kalatzis I. K. , Nikiforidis G. C. 2009. Segmentation of complementary DNA microarray images by wavelet-based Markov random ?eld model. IEEE Transactions on Information Technology in Biomedicine. 13(6), 1068-1074.
  12. Qin L. , Rueda L. , Ali A. , Ngom A. 2005. Spot detection and image segmentation in DNA microarray data. Applied Bioinformatics. 4, 1-11.
  13. Eisen M. B. 2013. ScanAlyze, Available: http://rana. lbl. gov/EisenSoftware. htm
  14. Chen Y. , Dougherty E. R. , Bittner M. L. 1997. Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics. 2(4), 364-374 .
  15. Adams R. , Bischof L. 1994. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 16(6), 641-647.
  16. Uslan V. , Bucak I. O. 2010. Microarray image segmentation using clustering methods. Mathematical and Computational Ap- plications. 15(2), 240-247.
  17. Maguluri L. P. , Rajapanthula K. , Srinivasu P. N. 2013. A com- parative analysis of clustering based segmentation algorithms in microarray images. International Journal of Emerging Science and Engineering (IJESE). 1(5), 27-32.
  18. Gonzalez R. C. , Woods R. E. , Eddins S. L. 2009. Digital Image Processing Using Matlab, second ed. , USA, 2009.
  19. EL-Allaoui A. , Nasri M. 2012. Medical image segmentation by Marker-controlled Watershed and mathematical morphol- ogy. The International Journal of Multimedia Its Applications (IJMA). 4(3), 245-254.
  20. Baraniak R. , Neelamani R. Weiner Filtering. from http://www. owlnet. rice. edu/ elec539/Projects99/BACH/proj2/wi ener. html
  21. Kumar S. , Kumar P. , Gupta M. , Nagawat A. K. 2010. Performance comparison of median and wiener ?lter in image denoising. International Journal of Computer Applications. 12(4), 27- 31.
  22. Nagesh A. S. , Varma G. P. S. , Govardhan A. 2010. An Improved Iterative Watershed and Morphological Transformation Techniques for Segmentation of Microarray Images. Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications. 2, 77-87.
  23. Kaur J. , Agrawal S. , Vig R. 2012. Performance analysis of clustering based image segmentation and optimization methods. Computer Science & Information Technology CS & IT 05. 2(2), 245-254.
Index Terms

Computer Science
Information Sciences

Keywords

cDNA Microarray Images Image Segmentation Marker Controlled Watershed

Powered by PhDFocusTM