CFP last date
15 March 2024
Reseach Article

Performance Analysis of Image Restoration Techniques for Dermoscopy Images

by Mehmet Ali Altuncu, Fidan Kaya G�lagiz, Fatma Selin Hangisi, Suhap Sahin
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 8
Year of Publication: 2017
Authors: Mehmet Ali Altuncu, Fidan Kaya G�lagiz, Fatma Selin Hangisi, Suhap Sahin
10.5120/ijais2017451637

Mehmet Ali Altuncu, Fidan Kaya G�lagiz, Fatma Selin Hangisi, Suhap Sahin . Performance Analysis of Image Restoration Techniques for Dermoscopy Images. International Journal of Applied Information Systems. 11, 8 ( Jan 2017), 15-19. DOI=10.5120/ijais2017451637

@article{ 10.5120/ijais2017451637,
author = { Mehmet Ali Altuncu, Fidan Kaya G�lagiz, Fatma Selin Hangisi, Suhap Sahin },
title = { Performance Analysis of Image Restoration Techniques for Dermoscopy Images },
journal = { International Journal of Applied Information Systems },
issue_date = { Jan 2017 },
volume = { 11 },
number = { 8 },
month = { Jan },
year = { 2017 },
issn = { 2249-0868 },
pages = { 15-19 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number8/958-2017451637/ },
doi = { 10.5120/ijais2017451637 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:04:32.765640+05:30
%A Mehmet Ali Altuncu
%A Fidan Kaya G�lagiz
%A Fatma Selin Hangisi
%A Suhap Sahin
%T Performance Analysis of Image Restoration Techniques for Dermoscopy Images
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 8
%P 15-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image enhancement and image restoration methods are widely used in most of the recent image processing studies. The main purpose of image enhancement is to remove noise from the image albeit different kinds. Therefore, in every field where image processing methods are used, image enhancement methods are more or less needed. And dermatological images, in which mostly image quality is inadequate most of the time, are one of the primary fields where these techniques are needed. Dermatologists that work in this field carry out the recording of wound images via a digital dermoscopy device. Later they realize the decision-making process using the image processing techniques through software. And in this study, the comparison of different image preprocessing methods is carried out in order to remove the effects of lighting and to make the software used give more accurate decisions.

References
  1. H. Kittler, H. Pehamberger, K. Wolff, “Diagnostic accuracy of dermoscopy”, The Lancet Oncology,vol. 3, no. 3, pp. 159–165, 2002.
  2. D. Piccolo, A. Ferrari, K. Peris, R. Daidone, B. Ruggeri, S. Chimenti, “Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study”, British Journal of Diagnosis, vol. 147, no. 3, pp. 481-486, 2002.
  3. Iyatomi, H., Oka, H., Hashimoto, M. 2005. An internet based melanoma diagnostic system - toward the practical application. In Proceedings of theIEEE Symposium onComputational Intelligence in Bioinformatics and Computational Biology.
  4. C. Massone, R. H. Wellenhof, V. A. Siess, G. Gabler, C. Ebner, H. P. Soyer, “Melanoma screening with cellular phones”, PLoS ONE, vol. 2, no. 5, pp. 1-4, 2007.
  5. Handyscope-mobile dermatoscope: Handyscope (2010). [Online]. Available:http://www.handyscope.net
  6. K. Tran, M. Ayad, J. Weinberg, A. Cherng, M. Chowdhury, S. Monir, M. El Hariri, C. Kovarik, “Mobile teledermatology in the developing world: Implications of a feasibility study on 30 Egyptian patients with common skn diseases”, Journal of the American Academy of Dermatology, vol. 64, no. 2, pp. 302-309, 2011.
  7. R. Asaid, G. Boyce, G. Padmasekara, “Use of a smartphone for monitoring dermatological lesions compared to clinical photography”, Journal of Mobile Technology in Medicine, vol. 1, no. 1, pp. 16-18, 2012.
  8. M. João, M. Vasconcelos, L. Rosado, “No-reference blur assessment of dermatological images acquired via mobile devices”, Image and Signal Processing, vol. 8509, pp. 350-357, 2014.
  9. F. Xie, Y. Lu, A. C. Bovik, Z. Jiang, R. Meng, “Application-driven no-reference quality assessment for dermoscopy images with multiple distortions”, IEEE Transaction on Biomedical Engineering, vol. 63, no. 6, pp. 1248-1256, 2016.
  10. Lei, Wang, Yuan Yibao, and Piao Weiying. "A Two-Step Robust Filter for Mean Line Extraction Based on the Median Filter" 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015.
  11. Sunil, K., Kasturiwale,H. 2016. Quality assessment of Median filtering techniques for impulse noise removal from digital images. In Proceedings of theInternational Conference on Advanced Computing and Communication Systems .
  12. Zhao, J., Botao, Z., Hongjun, L. 2011. A median filter FPGA with harvard architecture. In Proceedings of the International Conference on Information Science and Technology.
  13. A. Yang, "Research on the ultrasonic medical image filtering method to combine mathematical morphology with adaptive median filter", Electronic Measurement Technology, vol. 8, 2009.
  14. Li, L., Meng, X., Liang, X. 2013. Reduction of impulse noise in MRI images using block-based adaptive median filter.In Proceedings of theIEEE International Conference on Medical Imaging Physics and Engineering.
  15. Chenguang, Y., Liu, Y. 2010. Application of modified adaptive median filter for impulse noise. In Proceedings of theInternational Conference onIntelligent Control and Information Processing.
  16. Wang, C. Y., Li, L., Yang, F. P., Gong, F. 2010. A new kind of adaptive weighted median filter algorithm. 2010.In Proceedings of theInternational Conference on Computer Application and System Modeling .
  17. P.Geetha, B. Chitradevi, “Image denoising using adaptive weighted median filter in synthetic aperture radar images", International Journal of Computer Science and Information Technology Research, vol. 2, no. 3, pp. 413-420, 2014.
  18. Shapiro, L. G.,Stockman, G., "Computer Vision" 1st Edition: Prentice Hall, 2001.
  19. Gedraite, E. S., Murielle, H. 2011. "Investigation on the effect of a Gaussian Blur in image filtering and segmentation", In Proceedings of theELMAR.
  20. B. Marhaba, Z.Mourad, K. Wassim, "Image restoration using a combination of blind and non - blind deconvolution techniques", International Journal of Engineering Research & Science, vol. 2, no. 5, pp. 225-239, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Image Restoration Dermoscopy Images Image Quality Assessment