2017/02/23 by David Álvarez, David Alvarez, Alvarez, David +3
Computer Science · Engineering · Environmental Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Remote Sensing and LiDAR Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.1702.07333
Abstract submitted to arXiv as prerequisite to participate in the ISIC2017 Skin Lesion Segmentation Challenge
arxiv created 2017/02/23 · openalex publication_date 2017/02/23 · arxiv updated 2017/03/02 · openalex created_date 2017/03/16 · openalex updated_date 2026/07/28
This abstract briefly describes a segmentation algorithm developed for the ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of the competition is to perform a segmentation (in the form of a binary mask image) of skin lesions in dermoscopic images as close as possible to a segmentation performed by trained clinicians, which is taken as ground truth. This project only takes part in the segmentation phase of the challenge. The other 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 segmented region, and (4) calculation of a final score for each region, representing the likelihood of corresponding to a suitable lesion segmentation. The scores in step (4) are obtained by averaging the results of 2 different regression models using the scores of each region as input. Before using the algorithm these regression models must be trained using the training set of images and ground truth masks provided by the Competition. Steps 2 to 4 are repeated with an increasing number of clusters (and therefore the image is segmented into more regions) until there is no further improvement of the calculated scores.