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Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
B.D. Phulpagar, R.S. Bichkar
10.5120/ijca2015906186

B D Phulpagar and R S Bichkar. Article: Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms. International Journal of Computer Applications 126(9):29-39, September 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {B.D. Phulpagar and R.S. Bichkar},
	title = {Article: Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {126},
	number = {9},
	pages = {29-39},
	month = {September},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Image segmentation technique is the process of separating the foreground objects of different intensities from the background. Several authors have proposed different methods for segmentation of images into two classes, each one having different quality of segmentation. Yu et al [1]. used a GA approach to segmentation of 2-D images into two classes. We have extended this method to segment the images into multiple classes or multiple intensity levels (Four, Eight, Twelve and Sixteen greay Levels). The proposed GA-based approach gives us good results for original synthetic images and noisy images containing rectangular, elliptical and irregular-objects using morphological operations upto sixteen classes. The results obtained give 81% to 100% pixel classification accuracy for different types of noise (Gaussian, Speckle, salt and pepper and Poisson) and high noise levels (SNR ranging between 2.18 dB to 6.97 dB). The segmentation results obtained by using proposed GA-based method are good as compared to standard image segmentation FCM method with the increasing noise density of salt and pepper and Gaussian noises.

References

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Keywords

Genetic Algorithm, Multiple Intensity Levels, Image Segmentation.