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Multi-resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images
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Li, Guannan, Raza, Shan-e-Ahmed and Rajpoot, Nasir M. (2017) Multi-resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images. Medical Image Analysis, 37 . pp. 91-100. doi:10.1016/j.media.2017.01.006 ISSN 1361-8415.
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Official URL: http://doi.org/10.1016/j.media.2017.01.006
Abstract
It has been recently shown that recurrent miscarriage can be caused by abnormally high ratio of number of uterine natural killer (UNK) cells to the number of stromal cells in human female uterus lining. Due to high workload, the counting of UNK and stromal cells needs to be automated using computer algorithms. However, stromal cells are very similar in appearance to epithelial cells which must be excluded in the counting process. To exclude the epithelial cells from the counting process it is necessary to identify epithelial regions. There are two types of epithelial layers that can be encountered in the endometrium: luminal epithelium and glandular epithelium. To the best of our knowledge, there is no existing method that addresses the segmentation of both types of epithelium simultaneously in endometrial histology images. In this paper, we propose a multi-resolution Cell Orientation Congruence (COCo) descriptor which exploits the fact that neighbouring epithelial cells exhibit similarity in terms of their orientations. Our experimental results show that the proposed descriptors yield accurate results in simultaneously segmenting both luminal and glandular epithelium.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > QM Human anatomy R Medicine > RG Gynecology and obstetrics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||
Library of Congress Subject Headings (LCSH): | Endothelium -- Histology, Miscarriage -- Prevention, Uterus -- Physiology, Generative organs, Female | ||||||||||
Journal or Publication Title: | Medical Image Analysis | ||||||||||
Publisher: | Elsevier Science BV | ||||||||||
ISSN: | 1361-8415 | ||||||||||
Official Date: | April 2017 | ||||||||||
Dates: |
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Volume: | 37 | ||||||||||
Page Range: | pp. 91-100 | ||||||||||
DOI: | 10.1016/j.media.2017.01.006 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 26 January 2017 | ||||||||||
Date of first compliant Open Access: | 21 January 2018 |
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