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This abstract briefly describes a segmentation algorithm developed for theISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective ofthe competition is to perform a segmentation (in the form of a binary maskimage) of skin lesions in dermoscopic images as close as possible to asegmentation performed by trained clinicians, which is taken as ground truth.This project only takes part in the segmentation phase of the challenge. Theother phases of the competition (feature extraction and lesion identification)are not considered.  The proposed algorithm consists of 4 steps: (1) lesion image preprocessing,(2) image segmentation using k-means clustering of pixel colors, (3)calculation of a set of features describing the properties of each segmentedregion, and (4) calculation of a final score for each region, representing thelikelihood of corresponding to a suitable lesion segmentation. The scores instep (4) are obtained by averaging the results of 2 different regression modelsusing the scores of each region as input. Before using the algorithm theseregression models must be trained using the training set of images and groundtruth masks provided by the Competition. Steps 2 to 4 are repeated with anincreasing number of clusters (and therefore the image is segmented into moreregions) until there is no further improvement of the calculated scores.

k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge