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Updated: Jun 8, 2026
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
Published on: August 8, 2025
Unsupervised segmentation for digital dermoscopic images
Kajsa Møllersen1, Herbert M Kirchesch, Thomas G Schopf
1Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North-Norway, Tromsø, Norway. kajsa.mollersen@telemed.no
Background:
Skin cancer is among the most common types of cancer. Melanoma is the most fatal of all skin cancer types. The only effective treatment is early excision. Recognising melanoma is challenging both for general physicians and for expert dermatologists. A computer-aided diagnostic system improving diagnostic accuracy would be of great importance. Segmenting the lesion from the skin is the first step in this process.
Methods:
The present segmentation algorithm uses a multiscale approach for density analysis. Only the skin mode is found by density analysis and then the location of the lesion mode is estimated. The density estimates are attained by Gaussian kernel smoothing with several bandwidths. A new algorithm for hair recognition based on morphological operations on binary images is incorporated into the segmentation algorithm.
Results:
The algorithm provides correct segmentation for both unimodal and multimodal densities. The segmentation is totally unsupervised, with a digital image as the only input. The algorithm has been tested on an independent set of images collected in dermatological practice, and the segmentation is verified by three dermatologists.
Conclusion:
The present segmentation algorithm is fast and intuitive. It gives correct segmentation for most types of skin lesions, but fails when the lesion is brighter than the surrounding skin.
