The Discrete Analysis of the Tissue Biopsy Images with Metamaterial Formalization: Identifying Tumor Locus

Tatjana Gric, Sergei G Sokolovski, Alexander Alekseev, Andrian Mamoshin, Andrey Dunaev, Edik U. Rafailov

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number9363516
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number5
Early online date25 Feb 2021
Publication statusPublished - 1 Sept 2021

Bibliographical note

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Funding: This project has received funding from the European Union’s
Horizon 2020 research and innovation programme under the
Marie Sklodowska Curie grant agreement No 713694 and from
Engineering and Physical Sciences Research Council (EPSRC)
(Grant No. EP/R024898/1). E.U.R. also acknowledges partial
support from the Academic Excellence Project 5-100 proposed
by Peter the Great St. Petersburg Polytechnic University.


  • Cancer
  • metamaterial


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