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Proceedings Paper

Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin
Author(s): Paweł Kłeczek; Grzegorz Dyduch; Joanna Jaworek-Korjakowska; Ryszard Tadeusiewicz
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Paper Abstract

Background: Epidermis area is an important observation area for the diagnosis of inflammatory skin diseases and skin cancers. Therefore, in order to develop a computer-aided diagnosis system, segmentation of the epidermis area is usually an essential, initial step. This study presents an automated and robust method for epidermis segmentation in whole slide histopathological images of human skin, stained with hematoxylin and eosin. Methods: The proposed method performs epidermis segmentation based on the information about shape and distribution of transparent regions in a slide image and information about distribution and concentration of hematoxylin and eosin stains. It utilizes domain-specific knowledge of morphometric and biochemical properties of skin tissue elements to segment the relevant histopathological structures in human skin. Results: Experimental results on 88 skin histopathological images from three different sources show that the proposed method segments the epidermis with a mean sensitivity of 87 %, a mean specificity of 95% and a mean precision of 57%. It is robust to inter- and intra-image variations in both staining and illumination, and makes no assumptions about the type of skin disorder. The proposed method provides a superior performance compared to the existing techniques.

Paper Details

Date Published: 1 March 2017
PDF: 19 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400M (1 March 2017); doi: 10.1117/12.2249018
Show Author Affiliations
Paweł Kłeczek, AGH Univ. of Science and Technology (Poland)
Grzegorz Dyduch, Jagiellonian Univ. Medical College (Poland)
Joanna Jaworek-Korjakowska, AGH Univ. of Science and Technology (Poland)
Ryszard Tadeusiewicz, AGH Univ. of Science and Technology (Poland)


Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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