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dc.contributor.authorRaita-Hakola, Anna-Maria
dc.contributor.authorAnnala, Leevi
dc.contributor.authorLindholm, Vivian
dc.contributor.authorTrops, Roberts
dc.contributor.authorNäsilä, Antti
dc.contributor.authorSaari, Heikki
dc.contributor.authorRanki, Annamari
dc.contributor.authorPölönen, Ilkka
dc.date.accessioned2022-05-05T08:10:17Z
dc.date.available2022-05-05T08:10:17Z
dc.date.issued2022
dc.identifier.citationRaita-Hakola, A.-M., Annala, L., Lindholm, V., Trops, R., Näsilä, A., Saari, H., Ranki, A., & Pölönen, I. (2022). FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications. <i>Sensors</i>, <i>22</i>(9), Article 3420. <a href="https://doi.org/10.3390/s22093420" target="_blank">https://doi.org/10.3390/s22093420</a>
dc.identifier.otherCONVID_119008980
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/80902
dc.description.abstractHyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofseriesSensors
dc.rightsCC BY 4.0
dc.subject.otherhyperspectral
dc.subject.otherFPI
dc.subject.othercalibration
dc.subject.otherinterferometry
dc.subject.otheroptical modelling
dc.subject.otherconvolutional neural network
dc.subject.otherLED illumination
dc.subject.otherphotometric stereo
dc.subject.otherskin surface model
dc.subject.otherbiomedical imaging
dc.subject.otherdermatological application
dc.subject.otheroptical biopsy
dc.titleFPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202205052555
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1424-8220
dc.relation.numberinseries9
dc.relation.volume22
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber314519
dc.subject.ysoneuroverkot
dc.subject.ysohyperspektrikuvantaminen
dc.subject.ysoihosyöpä
dc.subject.ysodiagnostiikka
dc.subject.ysoihotaudit
dc.subject.ysoiho
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p39290
jyx.subject.urihttp://www.yso.fi/onto/yso/p13613
jyx.subject.urihttp://www.yso.fi/onto/yso/p416
jyx.subject.urihttp://www.yso.fi/onto/yso/p8746
jyx.subject.urihttp://www.yso.fi/onto/yso/p1769
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/s22093420
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundinginformationThis research was funded by the Academy of Finland, grant numbers 314519, 314520 and 314521.
dc.type.okmA1


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