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Abstract. Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients' quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

13Technological solutions regarding automated sorting of food according to their quality 14 parameters are of great interest to food industry. In this regard, automated sorting of fish rest 15 raw materials remains as one of the key challenges for the whitefish industry. Currently, the 16 sorting of roe, milt, and liver in whitefish fisheries is done manually. Automated sorting could 17 enable higher profitability, flexibility in production and increase the potential for high value 18 products from roe, milt and liver that can be used for human consumption. In this study, we 19 investigate and present a solution for classification of Atlantic cod (gadus morhua) roe, milt 20 and liver using visible and near-infrared hyperspectral imaging. Recognition and classification 21 of roe, milt and liver from fractions is a prerequisite to enabling automated sorting. 22Hyperspectral images of cod roe, milt and liver samples were acquired in the 400 -2500 nm 23 range and specific absorption peaks were characterized. Inter-and intra-variation of the 24 2 2 materials were calculated using spectral similarity measure. Classification models operating 25 on one and two optimal spectral bands were developed and compared to the classification 26 model operating on the full VIS/NIR (400 -1000 nm) range. Classification sensitivity of 70% 27 and specificity of 94% for one-band model, and 96% and 98% for two-band model 28 (sensitivity and specificity respectively) were achieved. Generated classification maps showed 29 that sufficient discrimination between cod liver, roe and milt can be achieved using two 30 optimal wavelengths. Classification between roe, milt and liver is the first step towards 31 automated sorting. 32

Imaging of vessel structures can be useful for investigation of endothelial function, angiogenesis and hypervascularization. This can be challenging for hyperspectral tissue imaging due to photon scattering and absorption in other parts of the tissue.Real-time processing techniques for enhancement of vessel contrast in hyperspectral tissue images were investigated. Wavelet processing and an inverse diffusion model were employed, and compared to band ratio metrics and statistical methods. A multiscale vesselness filter was applied for further enhancement.The results show that vessel structures in hyperspectral images can be enhanced and characterized using a combination of statistical, numerical and more physics informed models.