Open Access

ARTICLE

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

Rabia Javed^1,2, Mohd Shafry Mohd Rahim¹, Tanzila Saba³, Suliman Mohamed Fati³, Amjad Rehman^3,*, 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:

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

Received 08 October 2020; Accepted 22 December 2020; Issue published 05 February 2021

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.

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