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

Effect of Different Parameters on Segmentation of Binary Images using Genetic Algorithms

by B.D. Phulpagar, R.S. Bichkar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126 - Number 9
Year of Publication: 2015
Authors: B.D. Phulpagar, R.S. Bichkar
10.5120/ijca2015906185

B.D. Phulpagar, R.S. Bichkar . Effect of Different Parameters on Segmentation of Binary Images using Genetic Algorithms. International Journal of Computer Applications. 126, 9 ( September 2015), 19-28. DOI=10.5120/ijca2015906185

@article{ 10.5120/ijca2015906185,
author = { B.D. Phulpagar, R.S. Bichkar },
title = { Effect of Different Parameters on Segmentation of Binary Images using Genetic Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 126 },
number = { 9 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume126/number9/22580-2015906185/ },
doi = { 10.5120/ijca2015906185 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:59.839467+05:30
%A B.D. Phulpagar
%A R.S. Bichkar
%T Effect of Different Parameters on Segmentation of Binary Images using Genetic Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 126
%N 9
%P 19-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation is a process of separating the objects from background. Several GA-based approaches have been used to improve the quality of image segmentation in the past based on thresholds, edge detection, region and cluster detection and morphology. The proposed GA-based approach gives us good results for the segmentation of binary images using different image parameters namely the image block size 4×4, 8×8, and 16×16 pixels, the difference between the object and background intensities. Also the proposed approaches gives us best segmentation results for various GA parameters is considered that include population size (minimum 40), number of GA generations i.e. 80 and crossover rate more than 0.50 and less than or equal 0.70 and mutation rate between 0.01 and 0.08. The results obtained give 100% pixel classification accuracy for Speckle noise with noise levels more than 2.70 dB SNR and 93% to 96% for salt and pepper noise levels (SNR ranging between 7.69 dB to 11.92 dB). The segmentation results obtained by using proposed GA-based method are good with the increasing noise density of Speckle as compared to salt and pepper noises.

References
  1. C. Bradhurst, W. Boles, and Yin Xiao, “Segmentation of Bone Marrow Stromal Cells in Phase Contrast Microscopy Images,” IEEE 23rd International Conference on Image and Vision Computing, pp. 1 - 6, November - 2008.
  2. G. Bosco, “A Genetic Algorithm for Image Segmentation,” IEEE Computer Society, 11th International Conference on Image Analysis and Processing, pp. 262 - 266, September - 2001.
  3. B. Bhanu, J. Ming, and L. Sungkee, “Closed-loop Adaptive Image Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 734 - 735, June - 1991.
  4. R. S. Bichkar and A. K. Ray, “Tomographic Reconstruction of Circular and Elliptical Objects using Genetic Algorithm,” IEEE Signal Processing Letters, vol. 5, no. 10, pp. 241 - 251, October - 1998.
  5. M. Haseyama, N. Iwai, and H. Kitajima, “Ccriterion-based Image Segmentation Method with a Genetic Algorithm,” IEEE International Symposium on Circuits and Systems, vol. 4, pp. 94 - 97, July - 1999.
  6. R. Cornely and W. Kuklinski, “Application of Genetic Optimization to Medical Image Segmentation,” IEEE Conference on Bioengineering, pp. 76 - 79, March - 1994.
  7. Y. Horita, T. Murai, and M. Miyahara, “Region Segmentation using k-mean Clustering and Genetic Algorithms,” IEEE International Conference on Image Processing, vol. 4, pp. 1016 - 1020, 1994.
  8. P. Schroeter, J. Vesin, T. Langenberger, and R. Meuli, “Robust Parameter Estimation of Intensity Distributions for Brain Magnetic Resonance Images,” IEEE Transactions on Medical Imaging, vol. 71, pp. 172 - 186, April-1998.
  9. L. Cinque, S. Levialdi, G. Pignalberi, and R. Cucchiara, “Optimal Range Segmentation Parameters Through Genetic Algorithms,” IEEE 15th International Conference on Pattern Recognition, vol. 1, pp. 474 - 477, September-2000.
  10. B. D. Phulpagar and R. S. Bichkar, “Segmentation of Noisy Binary Images Containing Circular and Elliptical Objects using Genetic Algorithms”, International Journal of Computer Applications, vol. 66, no. 22, pp. 1 – 7, March – 2013.
  11. D. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Pearson, Delhi, 2004, 7th Edition.
  12. R. C. Gonzalez, R. E. Woods, Digital Image Processing, Pearson, Delhi, 2008, 3rdEdition.
  13. A. K. Jain, Fundamental of Digital Image Processing, Pearson, Delhi, 1989, 2nd Edition.
  14. E. Gose, R. C. Johnsonbaugh, Pattern Recognition and Image Analysis, PrenticeHall, Delhi, 2000, 2ndEdition.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm GA Parameters Image Parameters and Image Segmentation.

Powered by PhDFocusTM