Imaging of vessel structures can be useful for investigation of endothelial function, angiogenesis and hyper-vascularization. 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.
Information about the size and depth of a wound and how it is developing is an important prognostic tool in wound diagnostics. In this study a two-camera vision system has been developed to collect optical properties, shape and volume of chronic skin ulcers as tool for diagnostic assistance. This system combines the functionality of 2D imaging spectroscopy and 3D stereo-photogrammetry. A high resolution hyperspectral camera and a monochromatic video frame camera were mounted on the same scanning system. Stereo images were acquired to obtain information about the wound surface geometry. A Digital Surface Model (DSM) of the wound surface was reconstructed by applying stereophotogrammetric methods. The hyperspectral image was co-registered to the monochromatic frame image and the wound border was extracted by applying spectroscopic analysis (e.g. tissue oxygenation, pigmentation, classification). The resulting DSM of the undamaged surroundings of the wound was used to reconstruct the top surface above the wound and thus the wound volume. The analyses can, if desired, be limited to a certain depth of interest like the wound bed or wound border. Simultaneous analysis of the hyperspectral data and the surface model gives a promising, new, non-invasive tool for characterization of chronic wounds. Future work will concentrate on implementation of real time analysis and improvement of the accuracy of the system.
The healing process of chronic wounds is complex, and the complete pathogenesis is not known. Diagnosis is currently based on visual inspection, biopsies and collection of samples from the wound surface. This is often time consuming, expensive and to some extent subjective procedures. Hyperspectral imaging has been shown to be a promising modality for optical diagnostics. The main objective of this study was to identify a suitable technique for reproducible classification of hyperspectral data from a wound and the surrounding tissue. Two statistical classification methods have been tested and compared to the performance of a dermatologist. Hyperspectral images (400-1000 nm) were collected from patients with venous leg ulcers using a pushbroom-scanning camera (VNIR 1600, Norsk Elektro Optikk AS).Wounds were examined regularly over 4 - 6 weeks. The patients were evaluated by a dermatologist at every appointment. One patient has been selected for presentation in this paper (female, age 53 years). The oxygen saturation of the wound area was determined by wavelength ratio metrics. Spectral angle mapping (SAM) and k-means clustering were used for classification. Automatic extraction of endmember spectra was employed to minimize human interaction. A comparison of the methods shows that k-means clustering is the most stable method over time, and shows the best overlap with the dermatologist’s assessment of the wound border. The results are assumed to be affected by the data preprocessing and chosen endmember extraction algorithm. Results indicate that it is possible to develop an automated method for reliable classification of wounds based on hyperspectral data.
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