Examination of Skin Cancer Images using Wavelet De-noising
B. Vasantha Lakshmi1, D. Elizabath Rani2

1B. Vasantha Lakshmi, Researcher, Department of EECE, Gitam Deemed to be University, Visakhapatnam (Andhra Pradesh), India.

2Dr. D. Elizabeth Rani, Professor, Department of EECE, Gitam University, Visakhapatnam (Andhra Pradesh), India.

Manuscript received on 22 November 2019 | Revised Manuscript received on 10 December 2019 | Manuscript Published on 30 December 2019 | PP: 1-6 | Volume-9 Issue-2S3 December 2019 | Retrieval Number: B10011292S319/2019©BEIESP | DOI: 10.35940/ijitee.B1001.1292S319

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The objective of this paper is de-noising of melanoma images using wavelets because, dermatoscopy images are corrupted by noise, which leads to fault diagnosis. Hence de-noising is essential in melanoma skin cancer image to remove the salt and pepper noise(impulse noise) by preserving the melanoma image original information. The wavelet thresholding techniques are used in this paper to de-noise the melanoma image and improved the quality of an image. Wavelet de-noising algorithm has been developed employing soft and hard thresholding techniques. It works on Daubechies, Symlet, biorthogonal wavelets at decomposition level5. Image objective performance metrics like peak signal to noise ratio, mean square error and statistical performance metrics like mean, median, standard deviation, L1 norm, L2 norm are observed and analyzed for melanoma images.

Keywords: De-noising, Mean Square Error (MSE), Melanoma, Peak Signal to Noise Ratio (PSNR), Thresholding, Wavelet Transform.
Scope of the Article: Image Security