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dc.contributor.authorPölönen, Ilkka
dc.contributor.authorTuovinen, Tero
dc.contributor.authorPuupponen, Hannu-Heikki
dc.contributor.authorSalmivuori, Mari
dc.contributor.authorGrönroos, Mari
dc.contributor.authorNeittaanmäki, Noora
dc.contributor.editorTuovinen, Tero T.
dc.contributor.editorPeriaux, Jacques
dc.contributor.editorNeittaanmäki, Pekka
dc.date.accessioned2023-02-02T09:59:54Z
dc.date.available2023-02-02T09:59:54Z
dc.date.issued2022
dc.identifier.citationPölönen, I., Tuovinen, T., Puupponen, H.-H., Salmivuori, M., Grönroos, M., & Neittaanmäki, N. (2022). Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), <i>Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges</i> (pp. 153-176). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. <a href="https://doi.org/10.1007/978-3-030-70787-3_11" target="_blank">https://doi.org/10.1007/978-3-030-70787-3_11</a>
dc.identifier.otherCONVID_100292917
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/85301
dc.description.abstractFor accurate removal of malignant skin tumors, it is crucial to assure the complete removal of the lesions. In the case of certain ill-defined tumors, it is clinically challenging to see the true borders of the tumor. In this paper, we introduce several computationally efficient approaches based on spectral imaging to guide clinicians in delineating tumor borders. First, we present algorithms that can be used effectively with simulated skin reflectance data. By using simulated data, we gain detailed information about the sensitivity of the different approaches and how variables defined by algorithms act in the skin model. Second, we demonstrate the performance of the algorithms with spectral images taken in-vivo and representing two types of skin cancers with ill-defined borders, namely lentigo maligna and aggressive basal cell carcinoma. The results can be used as a guideline for developing software for the fast delineation of skin cancers.en
dc.format.extent275
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofComputational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges
dc.relation.ispartofseriesIntelligent Systems, Control and Automation: Science and Engineering
dc.rightsIn Copyright
dc.titleUnsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-202302021583
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.relation.isbn978-3-030-70786-6
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.description.reviewstatuspeerReviewed
dc.format.pagerange153-176
dc.relation.issn2213-8986
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Nature Switzerland AG 2022
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber314519
dc.subject.ysokarsinoomat
dc.subject.ysokonenäkö
dc.subject.ysospektrikuvaus
dc.subject.ysokoneoppiminen
dc.subject.ysokasvaimet
dc.subject.ysoihosyöpä
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28373
jyx.subject.urihttp://www.yso.fi/onto/yso/p2618
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p2299
jyx.subject.urihttp://www.yso.fi/onto/yso/p13613
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-70787-3_11
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundinginformationThis study was partly funded by the Academy of Finland (grant number 314519).
dc.type.okmA3


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