k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge
This work addresses segmentation for medical imaging in dermatology, but it is incremental as it builds on existing methods without introducing major innovations.
The paper tackled skin lesion segmentation in dermoscopic images by developing an algorithm that combines k-means clustering and an ensemble of regressions, achieving results close to clinician ground truth as part of the ISIC 2017 challenge.
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.