FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Sensors

Vivian Lindholm¹  Annamari Ranki¹  Anna-Maria Raita-Hakola²  Ilkka Pölönen²  Leevi Annala²  Roberts Trops³  Heikki Saari³  Antti Näsilä³ 

[1] Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland;Faculty of Information Technology, University of Jyväskylä, 40100 Jyväskylä, Finland;VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland;

关键词: hyperspectral;    FPI;    calibration;    interferometry;    optical modelling;    convolutional neural network;   

DOI  :  10.3390/s22093420

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown