Asian Journal of Computer Science and Technology (AJCST)
Identification of Virus in Microscopic Image Using Genetic Algorithm
Author : N. Senthilkumaran and R. PreethiVolume 8 No.2 Special Issue:March 2019 pp 24-27
Abstract
In this paper describes a several techniques of effective edge detection by using image segmentation. The image segmentation provides various techniques to detect the edges on image. The paper mainly focused on edge detection using matlab parameters and solved the many problems. Edge detection techniques have a several type of techniques. We have taken microscopic image, which affects the human body by making diseases through viruses and bacteria’s. Now analyze only about the major techniques: a.) Roberts edge detection, b) sobel edge detection, c) prewitt edge detection, d) log (laplacian of gaussian) edge detection, e) genetic edge detection and f) canny edge detection. We have applied above five techniques which are used in edge detection and got a result on microscopic images. Hence, we scope this paper defines and compares the variety of techniques and demand assures the genetic algorithm provides a better performance on edge detection using microscopic image.
Keywords
Edge Detection, Microscopic, Genetic Algorithm, Image Segmentation
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References
[1] P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. De Vries, M. J. Benders and I. Išgum, “Automatic segmentation of MR brain images with a convolutional neural network”, IEEE transactions on medical imaging. Vol. 35, No. 5, pp. 1252-61, May 2016
[2] A. Gooya, K.M. Pohl, M. Bilello, L. Cirillo, G. Biros, E.R. Melhem, and C. Davatzikos, “GLISTR: glioma image segmentation and registration”, IEEE transactions on medical imaging, Vol.31, No. 10, pp. 1941-54, Oct. 2012.
[3] Antonios Makropoulos, Ioannis S. Gousias, Christian Ledig, Paul Aljabar, Ahmed Serag, Joseph V. Hajnal, A. David Edwards, Serena J. Counsell, and Daniel Rueckert, “Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain”, IEEE transactions on medical imaging, Vol. 33, No. 9, September 2014.
[4] S. Lakshmi and D.V. Sankaranarayanan, “A study of edge detection techniques for segmentation computing approaches”, IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications”, CASCT, Vol. 20, pp. 35-40, Aug. 2010.
[5] N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
[6] Y. Ramadevi, T. Sridevi, B. Poornima, and B. Kalyani, “Segmentation and object recognition using edge detection techniques” International Journal of Computer Science & Information Technology (IJCSIT), Vol. 2, No. 6, Dec. 2010.
[7] A. Rosenfeld, “The Max Roberts Operator is a Hueckel-Type Edge Detector”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, Vol. 3, No. 1, pp. 101–103, February 1981.
[8] T. Singh, N. Kharma, M. Daoud, and R.Ward, “Genetic programming based image segmentation with applications to biomedical object detection”, In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM, pp. 1123-1130, 8 Jul 2009.
[9] A. Sheta, M.S. Braik, and S. Aljahdali, “Genetic algorithms: a tool for image segmentation”, In 2012 IEEE, International Conference on Multimedia Computing and Systems, pp. 84-90, May 10, 2012.
[10] K.R. Hole, V.S. Gulhane, N.D. Shellokar, “Application of genetic algorithm for image enhancement and segmentation”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 2, No. 4, pp. 1342, Apr. 2013.