Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging
This work addresses segmentation challenges in dermatology, but it is incremental as it builds on existing superpixel techniques.
The researchers tackled skin lesion segmentation in dermoscopic images by developing a superpixel merging method, achieving results comparable to state-of-the-art on the PH2 and ISIC 2017 datasets.
We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images. The segmentation is carried out by over-segmenting the original image using the SLIC algorithm, and then merge the resulting superpixels into two regions: healthy skin and lesion. The mean RGB color of each superpixel was used as merging criterion. The presented method is capable of dealing with segmentation problems commonly found in dermoscopic images such as hair removal, oil bubbles, changes in illumination, and reflections images without any additional steps. The method was evaluated on the PH2 and ISIC 2017 dataset with results comparable to the state-of-art.