# Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging

arXiv: https://arxiv.org/abs/1808.06759 · HTML: https://mlsanity.com/paper/1808.06759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs](https://mlsanity.com/paper/2309.01899/) (2023)
- [USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation](https://mlsanity.com/paper/2309.13289/) (2023)
- [A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation](https://mlsanity.com/paper/2311.12581/) (2023)
- [RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy](https://mlsanity.com/paper/2604.05594/) (2026)

## Abstract (arXiv)

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.