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An Adaptive Edge Detection for Mycosis Fungoides Skin Disease

by G. Ramakrishna, Debabrata Swain, Jore Sandeep S, K. Gopi Krishna
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 2
Year of Publication: 2014
Authors: G. Ramakrishna, Debabrata Swain, Jore Sandeep S, K. Gopi Krishna
10.5120/16316-5556

G. Ramakrishna, Debabrata Swain, Jore Sandeep S, K. Gopi Krishna . An Adaptive Edge Detection for Mycosis Fungoides Skin Disease. International Journal of Computer Applications. 94, 2 ( May 2014), 21-26. DOI=10.5120/16316-5556

@article{ 10.5120/16316-5556,
author = { G. Ramakrishna, Debabrata Swain, Jore Sandeep S, K. Gopi Krishna },
title = { An Adaptive Edge Detection for Mycosis Fungoides Skin Disease },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 2 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number2/16316-5556/ },
doi = { 10.5120/16316-5556 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:16:31.821556+05:30
%A G. Ramakrishna
%A Debabrata Swain
%A Jore Sandeep S
%A K. Gopi Krishna
%T An Adaptive Edge Detection for Mycosis Fungoides Skin Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 2
%P 21-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Edge is an important feature of an image and provides important image information that can be used for image interpretation. Many techniques of edge detection have been developed. This paper proposes an adaptive edge detection to accurately detect the most affected part of Mycosis Fungoides skin disease image. In this paper, this new method overcomes some common disadvantages of some traditional existing edge detection operators by properly detecting the boundary region of most affected part of skin disease. This proposed method compared with existing edge detection operators.

References
  1. Huiyu Zhou,Jiahua Wu,Jianguo Zhang,"DIGITAL IMAGE PROCESSING:PART 1", Ventus Publishing ApS ISBN 978-87-7681-541-7
  2. Trucco," Edge detection",Chapt4 AND Jain et al. , Chapt 5
  3. Rohit yadav, Ranbeer Tyagi, L. D. Malviya. "Low Magnitude Edge Detection Algorithm". International Journal of Computer Applications,Volume 23-No. 2,June 2011
  4. Sung KIM, "Applications of Convolution in Image Processing with MATLAB", University of Washington.
  5. Bernd Girod, "Digital Image Processing: Edge Detection", © 2013-2014 Stanford University .
  6. Haris Papasaika-Hanusch,"Digital Image Processing Using Matlab",Institute of Geodesy and Photogrammetry, ETH Zurich.
  7. Gonzalez and Woods, 2008. Digital Image Processing,Third Edition, Pearson Education.
  8. P. Thakare (2011) "A Study of Image Segmentation and Edge Detection Techniques", International Journal on Computer Science and Engineering, Vol 3,No. 2, 899-904.
Index Terms

Computer Science
Information Sciences

Keywords

Robert operator Prewitt operator Sobel operator zero crossing detector laplacian.

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