Abstract

Nowadays, transillumination imaging is more popular used in the medical field with the development of the vein finder and the non-invasive diagnosis applications. Near-infrared light with a wavelength of 700 - 1200 nm has relatively high transmission through biological tissue. Using near-infrared light, we can able to obtain a two dimensional (2D) transillumination image of the internal absorption structure such as blood vessel structure, liver ... in the body noninvasively. Even with a simple system (light-emitting diode (LED)'s array and low-cost camera), we could obtain the blood vessel transillumination image of human arm. However, the image is severely blurred due to the strong scattering in the tissue. We have devised the depth-dependent point spread function (PSF) to suppress the scattering effect in fluorescent imaging. In previous studies, we successfully applied this principle and developed a technique to reconstruct the absorbing structure in a turbid medium without using fluorescent material. The feasibility and effectiveness of the proposed technique were verified in experiments. However, this point spread function (PSF) is depth dependence, so that the depth information is required in practice. In order to make this method more practical, the new techniques for estimating the parameters of absorbing structure (depth and width) in the turbid medium by convolution and de-convolution with the point spread function (PSF) were devised. This paper presents a new technique for the estimation depth of an absorber in 2D transillumination image. This new technique was developed to estimate the depth of the absorber in turbid medium by convolution operation with the point spread function (PSF). By observing images with two-wavelength selected at which the scattering property of the medium is different. The transillumination image at one of the wavelengths is convolved with the PSF of another wavelength. Two images of alternative wavelengths are compared while changing the depth of the PSF. We can obtain the correct depth that gives a minimum difference between the two convoluted images. This technique does not require the repetition of the unstable deconvolution operation. The effectiveness of the proposed technique was verified in simulation and experiment.