Video-rate hyperspectral imaging with the Ultris X20

The Ultris X20 is a new and unique camera from Cubert GmbH and a real game- changer for hyperspectral imaging.

Based on light field

technology, the Ultris X20 provides video-rate imaging over 160 spectral bands covering the wavelength range from 350 to 1000nm. The camera combines a continuously variable bandpass filter, a multi-lens array and a 20-Megapixel Cmos sensor. The resulting spectral camera has a native resolution of 400 x 400 pixels, resulting in an impressive 160,000 pixels,

g Grégoire Mathieu, lead

each containing spectral data, and all collected in a snapshot!

The Ultris X20 Plus version

of the camera includes a second monochrome sensor to increase the spatial resolution to over 1800 x 1800 pixels using image fusion techniques. Powerful algorithms based on machine learning can be applied to the live data stream, allowing real-time classification and the retrieval of relevant information within seconds. Small and lightweight,

the Ultris X20 takes hyperspectral imaging to the next level. https://cubert-gmbh. com/product/ultris-x20- hyperspectral/

closely-related species.’ For this proof of concept,

a database of 2,253 colonies belonging to eight different species and three strains of Staphylococcus epidermidis was acquired. The optical setup and machine-learning analysis could classify all species with a correct identification rate of at least 91 per cent. The early-stage technology used in both studies was enabled, in part, by recent improvements in photonics components at CEA-Leti. The

“Mid-infrared imaging can provide unequivocal information”

next steps are to perform a dedicated prototype with the relevant wavelengths and to demonstrate the performance of the system with real-life samples, such as human biopsies, and to create larger databases for each application. EO

author of the tumour detection paper, said: ‘Employing recent developments in photonics components, which allow using infrared light to detect abnormal tissues, mid- infrared imaging can provide unequivocal information about the biochemical composition of human cells. ‘The combination of a set of

lasers and lensless imaging, with an uncooled bolometer matrix, allows biochemical mapping over a wide field of view. The project showed that this experiment’s setup, coupled

32 Electro Optics June 2021

to machine learning algorithms – Random Forest, Neural Networks, K-means – can help to classify the biological cells in a fast and reproducible way.’ The second technique is an optical-based, Petri- dish analysis using lensless multispectral mid-infrared imaging. The paper for that research states: ‘The technique relies on the acquisition of images at eight wavelengths corresponding to relevant chemical functions. It provides both morphological and discrete spectral data, which discriminates between even

Multispectral images of representative examples from the seven species of the database. Wavenumbers on top of each column are in cm-1

@electrooptics |


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