Melanoma Skin Cancer Detection by Segmentation and Feature Extraction using combination of OTSU and STOLZ Algorithm Technique

International Journal of Electronics and Communication Engineering

© 2017 by SSRG - IJECE Journal

Volume 4 Issue 4

Year of Publication : 2017

Authors : Nayana Banjan, Prajkta Dalvi and Neha Athavale

10.14445/23488549/IJECE-V4I4P105

How to Cite?

Nayana Banjan, Prajkta Dalvi and Neha Athavale, "Melanoma Skin Cancer Detection by Segmentation and Feature Extraction using combination of OTSU and STOLZ Algorithm Technique," SSRG International Journal of Electronics and Communication Engineering, vol. 4,  no. 4, pp. 21-25, 2017. Crossref, https://doi.org/10.14445/23488549/IJECE-V4I4P105

Abstract:

 Skin cancer exists in different forms like Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most dangerous and unpredictable. In this paper, we implement an image processing technique for the detection of Melanoma Skin Cancer using the software MATLAB which is easy for implementation as well as detection of Melanoma skin cancer. The input to the system is the skin lesion image. This image proceeds with the image pre-processing methods such as conversion of RGB image to Grayscale image, noise removal and so on. Further Otsu thresholding is used to segment the images followed by feature extraction that includes parameters like Asymmetry, Border Irregularity, Color and Diameter (ABCD) and then Total Dermatoscopy Score (TDS) is calculated. The calculation of TDS determines the presence of Melanoma skin cancer by classifying it as benign, suspicious or highly suspicious skin lesion.

Keywords:

Lesion, Melanoma, Otsu, STOLZ, TDS.

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