CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Efficient 2-D Structuring Element for Noise Removal of Grayscale Images using Morphological Operations

by Mangala A. G., Balasubramani R.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 6
Year of Publication: 2016
Authors: Mangala A. G., Balasubramani R.
10.5120/ijca2016910188

Mangala A. G., Balasubramani R. . Efficient 2-D Structuring Element for Noise Removal of Grayscale Images using Morphological Operations. International Journal of Computer Applications. 143, 6 ( Jun 2016), 24-28. DOI=10.5120/ijca2016910188

@article{ 10.5120/ijca2016910188,
author = { Mangala A. G., Balasubramani R. },
title = { Efficient 2-D Structuring Element for Noise Removal of Grayscale Images using Morphological Operations },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 6 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number6/25081-2016910188/ },
doi = { 10.5120/ijca2016910188 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:45:37.050712+05:30
%A Mangala A. G.
%A Balasubramani R.
%T Efficient 2-D Structuring Element for Noise Removal of Grayscale Images using Morphological Operations
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 6
%P 24-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mathematical morphological (MM) operations plays vital role in image processing and in enhancing the region of shape. Especially the application of basic morphological operations is used in enhancing the image quality. This paper describes an experiment with morphological operations for reducing the salt-and- pepper noise from the images of bacteria. The quality of enhanced images is measured based on image quality assessment operations. Experiment's results are given in comparison with various 2D-flat Structuring Element(SE).

References
  1. A. Taleb-Ahmed, X. Leclerc and T.S. Michel, 2001 Semi-Automatic Segmentation of Vessels by Mathematical Morphology: Application in MRI, Proceedings of International Conference on Image Processing, pp. 1063-1066.
  2. F. Ortiz, 2006 Gaussian Noise Removal by Color Morphology and Polar Color Models”, Proceedings of 3rd International Conference of Image Analysis and Recognition (ICIAR ‘06), Portugal, , pp. 163-172.
  3. J. Song, R. L. Stevenson, and E. J. Delp, 1989 The Use of Mathematical Morphology in Image Enhancement, Proceedings of the 32nd Midwest Symposium on Circuits and Systems, Urbana-Champaign, IL, pp. 67–70.
  4. J. Zhen, M. Zhong, L. Qi and W. Qinghua, 2004 Reducing Periodic Noise Using Soft Morphology Filter, Journal of Electronics (China), Vol. 21, No. 2, , pp. 159-162.
  5. J. Yan, G. Lu and H. Lu. 2001 A SAR Image Enhancement Technique Based on Morphological Wavelet Transformation, Proceedings of International Conference on Multispectral and Hyperspectral Image Acquisition and Processing, Wuhan, China, pp. 203-208.
  6. J-N. Chi, D-S. Wang, Y. Duan and X-H. Xu. 2005 Algorithm of Image Enhancement Based on Order Morphology Filtering and Image Entropy Difference, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 5105-5110.
  7. M. Seul, L. O’Gorman and M.J. Sammon, 2000 Practical Algorithms for Image Analysis. Cambridge University Press.
  8. M. Wirth and J. Lyon, 2005 Selective image enhancement using attribute morphology, Journal Imaging Science, Vol. 53, No. 4, Maney Publishing, pp. 195-198.
  9. Nursuriati Jamil & Tengku Mohd Tengku Sembok. 2003. Gradient-Based Edge Detection of Songket Motifs. Digital Libraries: Technology and Management of Indigeneous Knowledge, pp. 456-467, Springer-Verlag Berlin.
  10. Nursuriati Jamil, Zainab Abu Bakar & Tengku Mohd Tengku Sembok. 2004 A Comparison of Noise Removal Techniques in Songket Motif Images. IEEE Computer Society Conference on Computer Graphics, Image and Visualization (CGIV ‘04), pp. 39-143.
  11. P. Soille, 2002 Morphological Image Analysis, Springer Verlag, Berlin.
  12. R.A. Peters, 1995. A New Algorithm for Image Noise Reduction Using Mathematical Morphology, IEEE Trans. on Image Processing, Vol. 4, No.5, pp. 554-568.
  13. S. Tsekeridou, C. Kotropoulos and I. Pitas, 1996 Morphological Signal Adaptive Median Filter for Noise Removal, International Conference on Electronics, Circuits and Systems (ICECS 96), Rhodes, Greece, pp. 191-194.
  14. S. Umbaugh, 1999 Computer Vision and Image Processing. Prentice Hall Inc.
  15. www.mathswork.com. (http://in.mathworks.com/help/images/structuring-elements.html)
Index Terms

Computer Science
Information Sciences

Keywords

Mathematical morphology Structuring element Lactococcus.

Powered by PhDFocusTM