CFP last date
22 April 2024
Reseach Article

Implementing Morphological Operators for Edge Detection on 3D Biomedical Images

Published on March 2014 by Sadhana Singh, Ashish Agrawal, Shiv Kumar Vaish
International Conference on Advances in Computer Engineering and Applications
Foundation of Computer Science USA
ICACEA - Number 2
March 2014
Authors: Sadhana Singh, Ashish Agrawal, Shiv Kumar Vaish
931cfdfc-6fb9-40fd-aaf2-447a499d8b28

Sadhana Singh, Ashish Agrawal, Shiv Kumar Vaish . Implementing Morphological Operators for Edge Detection on 3D Biomedical Images. International Conference on Advances in Computer Engineering and Applications. ICACEA, 2 (March 2014), 48-52.

@article{
author = { Sadhana Singh, Ashish Agrawal, Shiv Kumar Vaish },
title = { Implementing Morphological Operators for Edge Detection on 3D Biomedical Images },
journal = { International Conference on Advances in Computer Engineering and Applications },
issue_date = { March 2014 },
volume = { ICACEA },
number = { 2 },
month = { March },
year = { 2014 },
issn = 0975-8887,
pages = { 48-52 },
numpages = 5,
url = { /proceedings/icacea/number2/15621-1416/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Computer Engineering and Applications
%A Sadhana Singh
%A Ashish Agrawal
%A Shiv Kumar Vaish
%T Implementing Morphological Operators for Edge Detection on 3D Biomedical Images
%J International Conference on Advances in Computer Engineering and Applications
%@ 0975-8887
%V ICACEA
%N 2
%P 48-52
%D 2014
%I International Journal of Computer Applications
Abstract

In this paper we describe the mathematical morphology in the form of high level image processing and mid level image processing. We study two approaches, for “color morphology” are vector approach and component-wise approach. In set theory approach, Mathematical morphology is developed by J.Serra and G. Matheron. Edge Detection is well known approach which aims at searching and detecting the points in a digital image at which the image brightness changes stridently. Edges are significant local changes of strength in an image. 3D biomedical images edge detection is an essential for object recognition of the human organs. Object recognition is a vital processing step in biomedical image segmentation. Important appearance can be extracted from the edges an image (e.g., corners, line, curves, etc.). In this paper, basic mathematical morphological operators are introduced at first then a mathematical edge detection algorithm is proposed to detect edges of the lungs CT image with salt-and-pepper noise and the Gaussian noise.

References
  1. David Dagan Feng, “Information Technology Applications in Biomedical Functional Imaging”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol. 3, NO.3, pp. 221-230. March 2000.
  2. Huertas, A. and Medioni, G., “Detection of intensity changes with sub pixel accuracy using Laplacian-Gaussian masks,” IEEE Trans.On Pattern Analysis and Machine Intelligence, PAMI, vol. 8, pp. 651–664, 1986.
  3. Zhao Fang, Ma Yulei, Zhang Junpeng, “Medical Image Processing on Mathematical Morphology”, 2nd International Conference on Computer Application and System Modelling, 2012.
  4. G. Matheron, Random Sets and Integral Geometry. New York: Wiley, 1974. J. Serra, Image Analysis and Mathematical Morphology, Vol. 1. London: Academic, 1982.
  5. J. Serra, Editor. Image Analysis and Mathematical Morphology, Vol. 2: Theoretical Advances. London: Academic, 1988.
  6. P. Maragos and R. W. Schafer, “Morphological filters—Part I: Their set theoretic analysis and relations to linear shift-invariant filters,” IEEETrans. Acoust., Speech, Signal Process., vol. 35, no. 8, pp. 1153-1169, 1987.
  7. Edge detection, Trucco, Chapt 4 AND Jain et al., Chapter 5
  8. https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm /
  9. Jonathan W. Valvano, John A. Pearce, Rebecca Richards-Kortum, Ronald E. Barr, J. K. Aggarwal, Mark Allen Schulze, Dissertation: “Biomedical Image Processing with Morphology-Based Nonlinear Filters”, The University of Texas at Austin December 1994.
  10. Aboul ella Hassanien, Ajith Abraham, “Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction”, International Journal of Computational Intelligence and Applications, ijcia2008.
  11. Aboul Ella Hassanien, Ajith Abraham, James F. Petersand Gerald Schaefer, “An Overview of Rough-Hybrid Approaches in Image Processing”.
Index Terms

Computer Science
Information Sciences

Keywords

3D Biomedical Images Edge Detection Morphological Operators Structural Element