Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding. (January 2019)

• Record Type: Journal Article
• Authors: Garcia-Arroyo, Jose Luis; Garcia-Zapirain, Begonya
• Is Part Of: Computer methods and programs in biomedicine. Volume 168(2019)
• Page Start: 11
• Page End: 19
• Publication Date: 2019-01
• Subjects: Pattern recognition -- Image processing -- Machine learning -- Border detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.11.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Innovative method based on fuzzy classification of pixels and subsequent histogram thresholding. Tested against two public databases, containing 379 and 600 images respectively, obtaining good results. Compared using the same databases and the same defined metrics with the state-of-the-art work. Abstract: Background and objective : To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem. Methods : The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding. Results : This method participated in the 2016 and 2017 ISBI (International Symposium on Biomedical Imaging) Challenges, hosted by the ISIC (International Skin Imaging Collaboration). It was tested against two public databases containing 379 and 600 images respectively, and compared using the same defined metrics (Accuracy, Dice Coefficient, Jaccard Index, Sensitivity and Specificity) with the rest of participating state-of-the-art work, obtaining good results: (0.934, 0.869, 0.791, 0.870 and 0.978) and (0.884, 0.760, 0.665, 0.869 and 0.923) respectively, ranking 9th and 15th out of a total of 21 and 28 participants respectively using the Jaccard Index (which was the indicator used as a basis for ranking) and the 1st in the 2017 Challenge using the Sensitivity. Conclusion : The method has been proven to be robust and reliable. It's main contribution is the very design of the algorithm, highly innovative, which could also be used to deal with other segmentation problems of a similar nature.