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Automated Epidermis Segmentation in Ultrasound Skin Images

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Innovations in Biomedical Engineering (IBE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 925))

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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.

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Acknowledgements

This research was supported partially by the Polish National Science Centre (Narodowe Centrum Nauki) grant No. UMO-2016/21/B/ST7/02236 and partially by the Polish Ministry of Science and Silesian University of Technology statutory financial support No. BK-209/RIB1/2018.

Author information

Authors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland

    Joanna Czajkowska & Paweł Badura

Authors
  1. Joanna Czajkowska
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  2. Paweł Badura
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Corresponding author

Correspondence to Joanna Czajkowska .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Department of Biosensors and Biomedical Signals Processing, Silesian University of Technology, Zabrze, Poland

    Ewaryst Tkacz

  2. Faculty of Biomedical Engineering, Department of Biosensors and Biomedical Signals Processing, Silesian University of Technology, Zabrze, Poland

    Marek Gzik

  3. Faculty of Biomedical Engineering, Department of Biosensors and Biomedical Signals Processing, Silesian University of Technology, Zabrze, Poland

    Zbigniew Paszenda

  4. Faculty of Biomedical Engineering, Department of Biosensors and Biomedical Signals Processing, Silesian University of Technology, Zabrze, Poland

    Ewa Piętka

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Czajkowska, J., Badura, P. (2019). Automated Epidermis Segmentation in Ultrasound Skin Images. In: Tkacz, E., Gzik, M., Paszenda, Z., Piętka, E. (eds) Innovations in Biomedical Engineering. IBE 2018. Advances in Intelligent Systems and Computing, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-15472-1_1

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