Depolarization metric spaces for biological tissues classification
Entity
UAM. Departamento de Anatomía, Histología y NeurocienciaPublisher
WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimDate
2020-08-01Citation
10.1002/jbio.202000083
Journal of Biophotonics 13.8 (2020): e202000083
ISSN
1864-063XDOI
10.1002/jbio.202000083Funded by
Agència de Gestió d'Ajuts Universitaris i de Recerca, Grant/Award Number: 2017-SGR-001500; Ministerio de Economía y Competitividad, Grant/Award Numbers: Fondos FEDER, RTI2018-097107-B-C31Project
Gobierno de España. RTI2018-097107-B-C31Editor's Version
https://doi.org/10.1002/jbio.202000083Subjects
biological tissue; biomedical; depolarization; imaging; Mueller matrix; polarimetry; MedicinaRights
© 2020 The Authors; © 2020 Journal of BiophotonicsAbstract
Classification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided-recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework, we study the potential of some polarimetric metrics, the so-called depolarization spaces, for the classification of biological tissues. The analysis is performed using 120 biological ex vivo samples of three different tissues types. Based on these data collection, we provide for the first time a comparison between these depolarization spaces, as well as with most commonly used depolarization metrics, in terms of biological samples discrimination. The results illustrate the way to determine the set of depolarization metrics which optimizes tissue classification efficiencies. In that sense, the results show the interest of the method which is general, and which can be applied to study multiple types of biological samples, including of course human tissues. The latter can be useful for instance, to improve and to boost applications related to optical biopsy.
Files in this item
Google Scholar:Van Eeckhout, Albert
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García-Caurel, Enric
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Ossikovski, Razvigor
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Lizana, Angel
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Rodríguez, Carla
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González-Arnay, Emilio
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Campos, Juan
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