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Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images

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Song, Tzu-Hsi, Sanchez Silva, Victor, Eldaly, Hesham and Rajpoot, Nasir M. (2017) Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images. IEEE Transactions on Biomedical Engineering, 64 (12). pp. 2913-2923. doi:10.1109/TBME.2017.2690863

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Official URL: http://doi.org/10.1109/TBME.2017.2690863

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Abstract

Assessment of morphological features of megakaryocytes (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of megakaryocytes, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei. It then employs a novel dual-channel active contour model to delineate the boundary of megakaryocytic cytoplasm by using different deconvolved stain channels. Compared to other recent models, the proposed framework achieves accurate results for both megakaryocytic nuclear and cytoplasmic delineation.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QP Physiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Megakaryocytes -- Differentiation -- Software, Bone marrow -- Histology, Biopsy
Journal or Publication Title: IEEE Transactions on Biomedical Engineering
Publisher: IEEE
ISSN: 0018-9294
Official Date: December 2017
Dates:
DateEvent
December 2017Published
4 April 2017Available
25 March 2017Accepted
Volume: 64
Number: 12
Page Range: pp. 2913-2923
DOI: 10.1109/TBME.2017.2690863
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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