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Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images

by Tahamina Yesmin, Harsh Lohiya, Pinaki Pratim Acharjya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 16
Year of Publication: 2023
Authors: Tahamina Yesmin, Harsh Lohiya, Pinaki Pratim Acharjya
10.5120/ijca2023922854

Tahamina Yesmin, Harsh Lohiya, Pinaki Pratim Acharjya . Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images. International Journal of Computer Applications. 185, 16 ( Jun 2023), 1-17. DOI=10.5120/ijca2023922854

@article{ 10.5120/ijca2023922854,
author = { Tahamina Yesmin, Harsh Lohiya, Pinaki Pratim Acharjya },
title = { Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 16 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number16/32777-2023922854/ },
doi = { 10.5120/ijca2023922854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:12.650914+05:30
%A Tahamina Yesmin
%A Harsh Lohiya
%A Pinaki Pratim Acharjya
%T Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 16
%P 1-17
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Edge is a basic and important piece of information that can be examined and manipulated by various edge detection methods. Edge detection is the process used in digital image processing to determine image boundaries and remove unwanted areas from digitised images. Edge detection generally filters out the important and useful information from the whole structural image. In this chapter, edge detection methods and their mathematical implementations have been compared through first-order edge detection operators like Sobel, Canny, Robert, Prewitt, etc. using marker-controlled watershed transformation. In morphological image processing, the edge detection algorithm includes functions such as edge and marker-controlled watershed segmentation. The edge detection techniques are applied to different medical images. Simulation of edge detection techniques has been carried out using MATLAB, and the comparison is made on the basis of statistical measurements.

References
  1. Kumar A, Chandra U. Comparative Analysis of Image Segmentation using Edge-Region Based Technique and Watershed Transform. International Journal of Latest Technology in Engineering, Management & Applied Science. 2012 May; 8(5): 1-4.
  2. Rameshbabu K, Mangesthu M.Edge Detection with Theory and Soft Reviews. International Research Journal of Engineering and Technology. 2019 February; 6(2): 947-956.
  3. Koplowitz J. On the Edge Location Error for Local Maximum and Zero-Crossing Edge Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1994 December; 16(12), 1207-1212.
  4. Lakshmi S, Sankaranarayanan V. A Study of edge detection techniques for segmentation computing approaches. Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications. Special Issue on CASCT. 2010; 1: 35-41.
  5. Marr D, Hildreth E. Theory of edge detection. Proc. Royal Society of London, B 207. 187–217.
  6. Adlakha A, Adlakha D, Tanwar R. Analytical Comparison between Sobel and Prewitt Edge Detection Techniques. International Journal of Scientific & Engineering Research. 2016 January: 7(1): 1482-1485.
  7. Deregeh F, Nezamabadi-Pour H. A new gravitational image edge detection method using edge explorer agents. Natural Computing, 2014, 13(1): 65-78.
  8. Xu Q, Varadarajan S, Chakrabarti C. A distributed Canny edge detector: algorithm and FPGA implementation. IEEE Transactions on Image Processing, 2014 July; 23(7): 2944-2960.
  9. Liu J, Tang Q, Yang W. Defects’ geometric feature recognition based on infrared image Advances in Engineering Research, volume 123 1207 edge detection. Infrared Physics & Technology. 2014; 67: 387-390.
  10. Ansari MA, Kurchaniya D, Dixit M. A Comprehensive Analysis of Image Edge Detection Techniques. International Journal of Multimedia and Ubiquitous Engineering. 2017; 12(11):, pp.1-12.
  11. Sappa AD, Dornaika F. An Edge-Based Approach to Motion Detection. Proc. Computational Science - ICCS 2006. 2006 May; 563 – 570.
  12. Rashmi, Kumar M, Saxena R. Algorithm and Technique on Various Edge Detection : A Survey. Signal & Image Processing An International Journal; 2013 Junu: 4(3):65-75.
  13. Joanna SM, Mascarenhas M. A Review on Different Methods of Image Segmentation. International Journal of Creative Research Thoughts. 2020 July; 8(7): 5245-5250.
  14. Farheen KS,Vineet R. An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm. Computer Engineering and Intelligent Systems. 2013; 4(6): 1-7.
  15. Acharjya PP, Das R, Ghoshal D. A Study on Image Edge Detection Using the Gradients. International Journal of Scientific and Research Publications. 2012 December; 2(12): 2250-3153.
  16. Wu Y, Li Q. The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient. Sensors. 2022; 22: 1-23.
  17. Acharjya PP, Santra S, Ghoshal D. An Improved Scheme on Morphological Image Segmentation Using the Gradients. International Journal of Advanced Computer Science and Applications, 2013; 4(2): 100-104.
  18. Ghoshal D, Acharjya PP. Effect of various spatial sharpening filters on the performance of the segmented images using watershed approach based on image gradient magnitude and direction. International Journal of Computer Applications, 2013; 82 (6), 19-25.
  19. Rizvi ST, Sandhu M.S, Fatima SE. Image Segmentation using Improved Watershed Algorithm. International Journal of Computer Science and Information Technologies. 2014; 5(2): 209-213.
  20. Sharma1 AK, Bala A. Marker-based watershed transformation for image segmentation. TJPRC International Journal. 2013 October; 3(4): 187-192.
  21. Mandiratta S. Nagpal PB, Chaudhary S, A Perlustration of Various Image Segmentation Techniques. International Journal of Computer Applications. 2016 April; 139(12): 26-31.
  22. Acharjya PP, Santra S, Ghoshal D, A Comparative Study on Image Segmentation Using Various Combinations of Spatial Filters and Structuring Elements in Watershed Algorithm. International Journal of Electronics Communication and Computer Engineering. 2013; 4(3): 935-939.
  23. Acharjya PP, Ghoshal D, An image matching method for digital images using morphological approach. International Journal of Computer and Information Engineering. 2014 May; 8 (5), 859-863.
  24. Chezian RM, Poobathy D. Edge Detection Operators: Peak Signal to Noise Ration Based Comparison. Internnational Journal of Image, Graphics and Signal Processing, 2014, 10, 55-61.
  25. Ramya KM, Mithun TP. Edge Connectivity Techniques for Image Analysis – A Survey. International Research Journal of Engineering and Technology. 2020 May; 7(5): 5374-5379.
Index Terms

Computer Science
Information Sciences

Keywords

Medical imaging image segmentation edge detection watershed algorithm markers.

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