Vessel contrast enhancement in hyperspectral images

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.

[1]  B Chance,et al.  Corrections for inhomogeneities in biological tissue caused by blood vessels. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  Bostjan Likar,et al.  Contrast enhancement of subcutaneous blood vessel images by means of visible and near-infrared hyper-spectral imaging , 2009, Medical Imaging.

[3]  Martina Meinke,et al.  Determination of optical properties of human blood in the spectral range 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions. , 2006, Journal of biomedical optics.

[4]  Guillermo Aguilar,et al.  Comparison of diffusion approximation and Monte Carlo based finite element models for simulating thermal responses to laser irradiation in discrete vessels , 2005, Physics in medicine and biology.

[5]  I Fridolin,et al.  Optical non-invasive technique for vessel imaging: II. A simplified photon diffusion analysis. , 2000, Physics in medicine and biology.

[6]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[7]  Asgeir Bjorgan,et al.  Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum Noise Fraction-Based Approach , 2015, Sensors.

[8]  W Verkruysse,et al.  Modelling light distributions of homogeneous versus discrete absorbers in light irradiated turbid media. , 1997, Physics in medicine and biology.

[9]  Asgeir Bjorgan,et al.  Estimation of skin optical parameters for real-time hyperspectral imaging applications , 2014, Journal of biomedical optics.

[10]  Carlo Tomasi,et al.  Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography , 2013, Biomedical optics express.

[11]  W Verkruysse,et al.  Diffuse-reflectance spectroscopy from 500 to 1060 nm by correction for inhomogeneously distributed absorbers. , 2002, Optics letters.

[12]  Alex J Walsh,et al.  In vivo hyperspectral imaging of microvessel response to trastuzumab treatment in breast cancer xenografts. , 2014, Biomedical optics express.

[13]  L. T. Norvang,et al.  Therapeutic response during pulsed laser treatment of port-wine stains: Dependence on vessel diameter and depth in dermis , 1995, Lasers in Medical Science.

[14]  Steven L Jacques,et al.  Optical assessment of cutaneous blood volume depends on the vessel size distribution: a computer simulation study , 2009, Journal of biophotonics.

[15]  Hui Li,et al.  Refractive index of human whole blood with different types in the visible and near-infrared ranges , 2000, Photonics West - Biomedical Optics.

[16]  Boris Majaron,et al.  Three-dimensional Monte Carlo model of pulsed-laser treatment of cutaneous vascular lesions. , 2011, Journal of biomedical optics.

[17]  Lise Lyngsnes Randeberg,et al.  Hyperspectral imaging of blood perfusion and chromophore distribution in skin , 2009, BiOS.

[18]  L. O. Svaasand,et al.  Collimated light sources in the diffusion approximation. , 2000, Applied optics.

[19]  Matija Milanič,et al.  Wavelet based feature extraction and visualization in hyperspectral tissue characterization. , 2014, Biomedical optics express.

[20]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .