Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery
Pölönen, 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.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 153-176). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_11
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2022Copyright
© Springer Nature Switzerland AG 2022
For 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.
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SpringerParent publication ISBN
978-3-030-70786-6Is part of publication
Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical ChallengesISSN Search the Publication Forum
2213-8986Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/100292917
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Research Council of FinlandFunding program(s)
Academy Programme, AoFAdditional information about funding
This study was partly funded by the Academy of Finland (grant number 314519).License
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