TY - JOUR
T1 - The Discrete Analysis of the Tissue Biopsy Images with Metamaterial Formalization
T2 - Identifying Tumor Locus
AU - Gric, Tatjana
AU - Sokolovski, Sergei
AU - Alekseev, Alexander
AU - Mamoshin, Andrian
AU - Dunaev, Andrey
AU - Rafailov, Edik
N1 - Funding Information:
This work was supported in part by the European Union?s Horizon 2020 research and innovation programme under the Marie Sklodowska Curie Grant Agreement 713694, and in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/R024898/1. The work of Edik U. Rafailov was supported by the Academic Excellence Project 5-100 proposed by Peter the Great St. Petersburg Polytechnic University through the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies. The work of Andrey Dunaev and Andrian V. Mamoshin supported by the Russian Science Foundation under Project No. 18-15-00201.
Publisher Copyright:
© 1995-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Herein, we develop an enhanced and automated methodology for detection of the tumour cells in fixed biopsy samples. Metamaterial formalism (MMF) approach allowing recognition of tumour areas in tissue samples is enhanced by providing an advanced technique to digitize mouse biopsy images. Thus, a colour-based segmentation technique based on the K-means clustering method is used allowing for a precise segmentation of the cells composing the biological tissue sample. Errors occurring at the tissue digitization steps are detected by applying MMF. Doing so, we end up with the robust, fully automated approach with no needs of the human intervention, ready for the clinical applications. The proposed methodology consists of three major steps, i. e. digitization of the biopsy image, analysis of the biopsy image, modelling of the disordered metamaterial. It is worthwhile mentioning, that the technique under consideration allows for the cancer stage detection. Moreover, early stage cancer diagnosis is possible by applying MMF.
AB - Herein, we develop an enhanced and automated methodology for detection of the tumour cells in fixed biopsy samples. Metamaterial formalism (MMF) approach allowing recognition of tumour areas in tissue samples is enhanced by providing an advanced technique to digitize mouse biopsy images. Thus, a colour-based segmentation technique based on the K-means clustering method is used allowing for a precise segmentation of the cells composing the biological tissue sample. Errors occurring at the tissue digitization steps are detected by applying MMF. Doing so, we end up with the robust, fully automated approach with no needs of the human intervention, ready for the clinical applications. The proposed methodology consists of three major steps, i. e. digitization of the biopsy image, analysis of the biopsy image, modelling of the disordered metamaterial. It is worthwhile mentioning, that the technique under consideration allows for the cancer stage detection. Moreover, early stage cancer diagnosis is possible by applying MMF.
KW - Cancer
KW - metamaterial
UR - http://www.scopus.com/inward/record.url?scp=85101799672&partnerID=8YFLogxK
U2 - 10.1109/JSTQE.2021.3061960
DO - 10.1109/JSTQE.2021.3061960
M3 - Article
AN - SCOPUS:85101799672
VL - 27
JO - IEEE Journal of Selected Topics in Quantum Electronics
JF - IEEE Journal of Selected Topics in Quantum Electronics
SN - 1077-260X
IS - 5
M1 - 9363516
ER -