A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods

  • Hanae Moussaoui sidi mohamed ben abdellah university of Fez, Sciences and technologies Faculty of Fez
  • Nabil El Akkad Sidi Mohamed Ben Abdellah University of fez, Morocco
  • Mohamed Benslimane Sidi Mohamed Ben Abdellah University of fez, Morocco
DOI: https://doi.org/10.19139/soic-2310-5070-1549
Keywords: Image segmentation, skin cancer, fuzzy c-means, multi-Otsu, thresholding, marker-controlled watershed

Abstract

Presently image segmentation remains the most crucial stage in the image processing system. The main idea ofimage segmentation is to partition or divide a random image into several partitions depending on the problem to solve. In this paper, we will be presenting a new method of skin cancer detection based on Otsu’s thresholding algorithm and markercontrolled watershed method. This hybridization process is first of all started by segmenting the input image using fuzzy c-means algorithm which is a clustering method that gives the possibility to a pixel to belong to one or more clusters. After that, we will apply multi-Otsu which is a thresholding algorithm that separates the pixels of an image into a variety of classes depending on the intensity of the gray levels. The next step of this proposed method is the marker-controlled watershedalgorithm that divides the image into homogenous areas or regions by using edge-detection concepts including mathematical morphology. The proposed technique was applied and experienced using several images of different types of skin cancer that were collected and gathered from the web and also from the Kaggle dataset. To assess the worth of the achieved results, we used several evaluation metrics like dice coefficient, sensitivity, specificity as well as Jaccard similarity that all have shown good and satisfactory results.

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Published
2023-01-23
How to Cite
Moussaoui, H., Nabil El Akkad, & Mohamed Benslimane. (2023). A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods. Statistics, Optimization & Information Computing, 11(1), 95-105. https://doi.org/10.19139/soic-2310-5070-1549
Issue
Vol 11 No 1 (2023): SI-ICECS’21
Section
Research Articles
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