@inproceedings{552f81261003407992b4e79dc9babaf8,
title = "Automated epidermis segmentation in ultrasound skin images",
abstract = "The automated system for epidermis segmentation in ultrasound images of skin is described in this paper. The method consists of two main parts: US probe membrane segmentation and epidermis segmentation. The fuzzy C-means clustering is employed at the initial stage leading to probe membrane segmentation using fuzzy connectedness technique. Then, the upper (external) epidermis boundary is detected and adjusted using connectivity and line variability analysis. The lower (internal) boundary is obtained by shifting the upper edge by a constant vertical width determined adaptively during the experiments. The method is evaluated using a dataset of 13 US images of two registration depths. The validation relies on a ground truth delineations of the epidermis provided by two independent experts. The mean Hausdorff distances of 0.118 mm and 0.145 mm were obtained for the external and internal epidermis boundaries, respectively, with the mean Dice index for the epidermis region at 0.848.",
keywords = "High-resolution ultrasound, Image segmentation, Skin imaging, Skin layers",
author = "Joanna Czajkowska and Pawe{\l} Badura",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Conference on Innovations in Biomedical Engineering, IBE 2018 ; Conference date: 18-10-2018 Through 20-10-2018",
year = "2019",
doi = "10.1007/978-3-030-15472-1\_1",
language = "English",
isbn = "9783030154714",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "3--11",
editor = "Ewaryst Tkacz and Marek Gzik and Zbigniew Paszenda and Ewa Pietka",
booktitle = "Innovations in Biomedical Engineering, IBE 2018",
address = "Germany",
}