FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications
Raita-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. Sensors, 22(9), Article 3420. https://doi.org/10.3390/s22093420
Published in
SensorsAuthors
Date
2022Discipline
Computing, Information Technology and MathematicsLaskennallinen tiedeTietotekniikkaComputing, Information Technology and MathematicsComputational ScienceMathematical Information TechnologyCopyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Hyperspectral 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.
...
Publisher
MDPI AGISSN Search the Publication Forum
1424-8220Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/119008980
Metadata
Show full item recordCollections
Related funder(s)
Research Council of FinlandFunding program(s)
Academy Programme, AoFAdditional information about funding
This research was funded by the Academy of Finland, grant numbers 314519, 314520 and 314521.License
Related items
Showing items with similar title or keywords.
-
Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours : A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks
Lindholm, Vivian; Raita-Hakola, Anna-Maria; Annala, Leevi; Salmivuori, Mari; Jeskanen, Leila; Saari, Heikki; Koskenmies, Sari; Pitkänen, Sari; Pölönen, Ilkka; Isoherranen, Kirsi; Ranki, Annamari (MDPI AG, 2022)Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. ... -
Discriminating Basal Cell Carcinoma and Bowen’s Disease with Novel Hyperspectral Imaging System and Convolutional Neural Networks
Salmivuori, Mari; Lindholm, Vivian; Annala, Leevi; Raita-Hakola, Anna-Maria; Jeskanen, Leila; Pölönen, Ilkka; Koskenmies, Sari; Pitkänen, Sari; Isoherranen, Kirsi; Ranki, Annamari (Society for Publication of Acta Dermato-Venereologica, 2022) -
Discriminating basal cell carcinoma and Bowen's disease from benign skin lesions with a 3D hyperspectral imaging system and convolutional neural networks
Lindholm, Vivian; Annala, Leevi; Koskenmies, Sari; Pitkänen, Sari; Isoherranen, Kirsi; Järvinen, Anna; Jeskanen, Leila; Pölönen, Ilkka; Ranki, Annamari; Raita‐Hakola, Anna‐Maria; Salmivuori, Mari (Wiley-Blackwell, 2024) -
Differentiating Malignant from Benign for Melanocytic and Non-melanocytic Skin Tumors : A Pilot Study on Hyperspectral Imaging and Convolutional Neural Networks
Lindholm, Vivian; Raita-Hakola, Anna-Maria; Annala, Leevi; Salmivuori, Mari; Jeskanen, Leila; Koskenmies, Sari; Pitkänen, Sari; Saari, Heikki; Pölönen, Ilkka; Isoherranen, Kirsi; Ranki, Annamari (Society for Publication of Acta Dermato-Venereologica, 2022) -
Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks
Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include ...