  Lesion segmentation from the surrounding skin is the first task for
developing automatic Computer-Aided Diagnosis of skin cancer. Variant features
of lesion like uneven distribution of color, irregular shape, border and
texture make this task challenging. The contribution of this paper is to
present and compare two different approaches to skin lesion segmentation. The
first approach uses watershed, while the second approach uses mean-shift.
Pre-processing steps were performed in both approaches for removing hair and
dark borders of microscopic images. The Evaluation of the proposed approaches
was performed using Jaccard Index (Intersection over Union or IoU). An
additional contribution of this paper is to present pipelines for performing
pre-processing and segmentation applying existing segmentation and
morphological algorithms which led to promising results. On average, the first
approach showed better performance than the second one with average Jaccard
Index over 200 ISIC-2017 challenge images are 89.16% and 76.94% respectively.
