Open Access iconOpen Access

ARTICLE

crossmark

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Rabia Javed1,2, Mohd Shafry Mohd Rahim1, Tanzila Saba3, Suliman Mohamed Fati3, Amjad Rehman3,*, Usman Tariq4

1 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia Johor Bahru, 81310, Malaysia
2 Department of Computer Science, Lahore College for Women University, Lahore, 54000, Pakistan
3 College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11589, Saudi Arabia
4 College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia

* Corresponding Author: Amjad Rehman. Email: email

Computers, Materials & Continua 2021, 67(2), 2337-2352. https://doi.org/10.32604/cmc.2021.014677

Abstract

Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma. Three publicly available benchmark skin lesion datasets, ISIC 2017, ISBI 2016, and PH2, are used for the experiments. Currently, the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets. These datasets’ pre-analysis is necessary to overcome contrast variations, under or over segmented images boundary extraction, and accurate skin lesion classification. In this paper, we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets. The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images. The two performance measures, processing time and efficiency, are computed for evaluation of the proposed method. Our results showed that the proposed methodology improves the pre-processing efficiency of 77% of ISIC 2017, 67% of ISBI 2016, and 92.5% of PH2 datasets.

Keywords


Cite This Article

APA Style
Javed, R., Rahim, M.S.M., Saba, T., Fati, S.M., Rehman, A. et al. (2021). Statistical histogram decision based contrast categorization of skin lesion datasets dermoscopic images. Computers, Materials & Continua, 67(2), 2337-2352. https://doi.org/10.32604/cmc.2021.014677
Vancouver Style
Javed R, Rahim MSM, Saba T, Fati SM, Rehman A, Tariq U. Statistical histogram decision based contrast categorization of skin lesion datasets dermoscopic images. Comput Mater Contin. 2021;67(2):2337-2352 https://doi.org/10.32604/cmc.2021.014677
IEEE Style
R. Javed, M.S.M. Rahim, T. Saba, S.M. Fati, A. Rehman, and U. Tariq "Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images," Comput. Mater. Contin., vol. 67, no. 2, pp. 2337-2352. 2021. https://doi.org/10.32604/cmc.2021.014677

Citations




cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2133

    View

  • 1364

    Download

  • 0

    Like

Share Link