الفهرس 
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Abstract
The optical properties of a tissue affect both diagnostic and therapeutic applications of light. Hence, specifying the optical properties of a tissue is the first step toward properly designing devices, interpreting diagnostic measurements or planning therapeutic protocols. Diffuse optical techniques utilize light in the near infrared spectral range to measure tissue physiology non-invasively. Light is detected after passing through relatively thick tissue and optical properties of the tissue are spatially reconstructed using mathematical models. The propagation of photons through turbid media such as biological tissue can be modeled as a diffusion process. A mathematical description of the absorption and scattering characteristics of light can be performed in two different ways; analytical theory (based on the physics of Maxwell’s equations.) or, transport theory (without taking Maxwell’s equations into account). However, dense scattering in turbid media creates a complex superposition of waves and boundary conditions that cannot easily be solved analytically. As a result, we refer to the transport theory of photons, which treats light as non-interacting particles. The radiative transfer equation (RTE or Boltzmann equation) is an approximation to Maxwell’s equations, based upon conservation of energy, describing the propagation of photons and energy through space and time. The diffusion approximation to the radiative transport equation is the most widely used model to describe the migration of light through biological tissues. The aim of this work was to improve tissue monitoring with diffuse optics through implementing different analytical methods. Different biological tissue samples was monitored to determine their optical parameters including native and coagulated chicken liver, native, boiled and dry breast chicken skin. Diffuse reflectance and transmittance of the examined samples were measures using three different experimental setups including single integrating sphere, distant-detector, spatial frequency domain imaging based optical setups. Furthermore, the optical parameters were estimated from the experimentally collected measurements using a combination between Kubellka-Munk model and Beer’s law. This combination made it possible to calculate the whole three optical parameters “absorption coefficient, scattering coefficient and the anisotropy” of the examined tissues from the experimentally collected data. The estimated values of the optical parameters were then validated using Monte-Carlo simulation method. Moreover, the obtained values of the optical parameters were then introduced to the diffusion equation to calculate the fluence rate distribution at the tissue surface. Then, the optical fluence rate distribution images were created using the finite element solution of the diffusion equation. The resultant fluence rate distribution images have discriminant features between different tissue types. Therefore, this work provides a new method that can be employed for monitoring, differentiating and diagnosing biological tissues.