By James G. Fujimoto, Daniel Farkas
Biomedical optical imaging is a quickly rising learn region with frequent primary study and medical functions. This e-book supplies an outline of biomedical optical imaging with contributions from prime overseas examine teams who've pioneered lots of those recommendations and functions. a different learn box spanning the microscopic to the macroscopic, biomedical optical imaging permits either structural and useful imaging. concepts reminiscent of confocal and multiphoton microscopy supply mobile point answer imaging in organic platforms. the mixing of this know-how with exogenous chromophores can selectively improve distinction for molecular goals in addition to provide useful details on tactics resembling nerve transduction. Novel options combine microscopy with cutting-edge optics expertise, and those contain spectral imaging, photon fluorescence correlation, nonlinear nanoscopy; optical coherence tomography innovations permit practical, dynamic, nanoscale, and cross-sectional visualization. relocating to the macroscopic scale, spectroscopic evaluation and imaging equipment corresponding to fluorescence and light-weight scattering delivers diagnostics of tissue pathology together with neoplastic adjustments. strategies utilizing gentle diffusion and photon migration are a way to discover strategies which happen deep within organic tissues and organs. the mixing of those options with exogenous probes allows molecular particular sensitivity.
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Additional resources for Biomedical optical imaging
8 shows typical representations of these three spaces (image space [left], spectral space [middle], feature space [right]). Most computational processing methods for spectral imaging presented in this book deal with the mapping between spectral space, feature space, and image space. Spectral Signature and Spectral Similarity Measures The term spectral signature in this book is another name for a plot of the variations in absorbed, reflected, or emitted light intensity as a function of wavelength, time, and other possible scale units for each pixel in the image for a given imaging mode.
Correlation Measure Correlation, also called correlation coefficient, indicates the strength and direction of a linear relationship between two random variables. A number of different coefficients are used for different situations. The correlation is one in the case of a perfect linear relationship, and zero if the variables are independent. Spectral Correlation Similarity Spectral correlation similarity (SCS) uses the Pearson correlation coefficient as a similarity measure. The Pearson coefficient can be measured using the following equation: ⎞ ⎛ N ri − µref pi − µsamp ⎟ ⎜ 1 ⎟ ⎜ i=1 ·⎜ SCS = ⎟ ⎠ N −1 ⎝ σref σsamp where µref and σref represent the mean and standard deviation of the reference signature vector, and µsamp and σsamp represent those of the sample pixel vector.
12) where αρ and βφ are the strengths of the radial and azimuthal components, respectively. The vectors aρ = (cos φ, sin φ) and aϕ = (− sin φ, cos φ) represent unit vectors in the radial and azimuthal directions. 13) exp ( j kz cos θ) dθdφ where rp , φp , z denotes a point in the focal region k = 2πn/λ, where λ is the wavelength and n is the refractive index. Furthermore, αis the angular limit of integration determined by the NA (n sin α) of the objective lens. 13. We shall now specialize to the case of the focusing action of an objective lens when radially polarized light is incident (fig.
Biomedical optical imaging by James G. Fujimoto, Daniel Farkas