Graph weighting scheme for lesion segmentation in macroscopic images

Published: 29 April 2020| Version 2 | DOI: 10.17632/xs7yr2jwz7.2
Idir Filali


We present the results obtained by our graph weighting scheme based approach for skin lesion segmentation in macroscopic images on two datasets used by amelard et al. (Amelard, J. Glaister, A. Wong and D. A. Clausi, High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description, IEEE Trans. Biomed. Eng., 62(3) (2015), 820-831). The first is a subset of a DermIS database (Dermatology Information System, available online: that contains 43 photographs with lesion diagnosed as melanoma and 26 diagnosed as non melanoma. The second is a subset of the DermQuest database (Available online: that contains 76 images of melanoma lesions and 61 images of non melanoma lesions. The images are subject to various artifacts like a drastic shadow effect, the variation of illumination and poor contrast between the lesion and skin parts. We also provide the ground truth segmentation to evaluate visually the accuracy of our results.



Universite Mouloud Mammeri de Tizi Ouzou


Biomedical Engineering, Computer Vision Algorithms