With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Filter-On-Chip CMOS sensor equipped cameras are a convenient, reliable and affordable approach for the parallel acquisition of spatial and spectral information. The combination of pixel-arranged spectral filter matrices on CMOS sensors increases their integration density and system complexity by several times compared to standard RGB cameras. Due to their system design, these cameras have an increased spectral crosstalk and specific dependencies from the angle of radiation. The paper will show how to develop and set up a measurement arrangement for the characterization of the channel specific spectral sensitivities under different angles of radiation. These characterizations are necessary to develop a more robust model for the camera pixel-value correction to ensure the comparability and reproducibility of the measured values. Therefore, a measurement setup to investigate the influence of the angle of incident radiation on filter-on-chip CMOS sensors was developed. After initial investigations with a setup in which the camera was simply rotated and investigations with a lens and changed f-number confirmed that the angle influence results in a measurable difference in the sensor response, a new measurement arrangement was developed to investigate this behavior more precisely. The developed measurement arrangement allows multispectral resolving image sensors to be radiated with collimated light at reproducible angles of incidence and with adjustable wavelengths. By comparing the measured values with illuminances measured using a calibrated photodiode in the same setup and with the same parameters, it is possible to evaluate the angle dependence based on quantum efficiency curves according to the EMVA 1288 standard. The investigations carried out, the developed principles and the realized semi-automatic measurement arrangement will be shown and explained to characterize the capabilities of multispectral resolving filter-on-chip CMOS sensor equipped cameras for applications in industry and biomedicine.

Aim of the paper is the orientation of research and development on a completely new approach to innovative in-field and point of care diagnostics in industry, biology and medicine. Central functional modules are smartphones and/or smart pads supplemented by additional hardware apps and software apps. Specific examples are given for numerous practical applications concerning optodigital instrumentations. The methodical classification distinguishes between different levels for combination of hardware apps (hwapps) and software apps (swapps) with smartphones and/or smartpads. These methods are fundamental enablers for the transformation from stationary conventional laboratory diagnostics into mobile innovative in-field and point of care diagnostics. The innovative approach opens so far untapped enormous markets due to the convenience, reliability and affordability of smartphone and/or smartpad instruments. A highly visible advantage of smartphones and/or smartpads is the huge number of their distribution, their worldwide connectivity via cloud services and the experienced capability of their users for practical operations.

One way to increase the amount of information acquired via hyperspectral imaging and therefore to increase the possibility of data analysis is combining the spatial and spectral information of hyperspectral data sets. The aforementioned data sets are obtained by cameras covering different spectral ranges. The purpose of this article is to develop an algorithm which is able to combine two data sets acquired by two hyperspectral pushbroom imagers, covering the visible (VIS) and the near infrared (NIR) wavelength range. Initially, the effect of optical aberrations, as well as errors via the image registration were examined. Subsequently a correction algorithm for both the optical aberration and the image registration is elaborated.

In the development and optimization of biotechnological cultivation processes the continuous monitoring through the acquisition and interpretation of spectral and morphological properties of bioparticles are challenging. There is therefore a need for the parallel acquisition and interpretation of spatially and spectrally resolved measurements with which particles can be characterized and classified in-flow with high throughput. Therefore, in this paper we investigated the scientific and technological connectivity of standard imaging flow cytometry (IFC) with filter-on-chip based spatially and spectrally resolving snapshot-mosaic cameras for photonic sensing and control in a smart and innovative microfluidic device. For the investigations presented here we used the microalgae Haematococcus pluvialis (HP). These microalgae are used commercially to produce the antioxidant keto-carotenoid astaxanthin. Therefore, HP is relevant to practically demonstrate the usability of the developed system for Multispectral Imaging Flow Cytometry (MIFC) platform. The extension of standard IFC with snapshot-mosaic cameras and multivariate data processing is an innovative approach for the in-flow characterization and derived classification of bioparticles. Finally, the multispectral data acquisition and the therefore developed methodology is generalizable and enables further applications far beyond the here characterized population of HP cells.

For the objective comparison of different cameras or image sensors, it is necessary to describe their image acquisition channel by suitable parameters. The EMVA 1288 standard, developed by camera manufacturers and research institutes, distinguishes itself from other standards by considering the camera as a linear model. The camera is treated as a black box of which only pixel size and exposure time must be known. The recording of standardized test images is also omitted, allowing the camera to be described without optics. The only input variable of the linear camera model of the EMVA 1288 standard is the number of photons that hit a pixel of the image sensor during the exposure time. Therefore, the correct determination of the photon count is essential to calculate important camera parameters from the linear camera model, such as quantum efficiency or signal-to-noise ratio. To determine the number of photons, the irradiance of the radiation incident on the image sensor must be measured. This is usually done using a radiometer instead of the camera or image sensor. The number of photons per pixel during the exposure time can then be calculated from the irradiance, considering constants like the wavelength of the incident radiation, the area of the pixel and the exposure time of the camera. Calibrated radiometers were procured which are sensitive in the UV-A, VISNIR and NIR wavelength ranges in order to be able to perform camera or image sensor characterizations according to the EMVA 1288 standard in these wavelength ranges in the future.