A priori fluorophore distribution estimation in fluorescence imaging through application of a segmentation process and a data fitting technique.

During the last few years a quite large number of fluorescence imaging applications have been reported in the literature, as one of the most challenging problems in medical imaging is to "see" a tumor embedded in tissue, which is a turbid medium. This problem has not been fully encountered yet, due to the non-linear nature of the inverse problem. In this paper, a novel method for processing the forward solver outcomes is presented. Through this technique the comparison between the simulated and the acquired data can be performed only at the region-of-interest, minimizing time-consuming pixel-to-pixel comparison. With this modus operandi a-priori information about the initial fluorophore distribution becomes available, leading to a more feasible inverse problem solution.

A binocular machine vision system for three-dimensional surface measurement of small objects.

Rendering three-dimensional information of a scene from optical measurements is very important for a wide variety of applications. However, computer vision advancements have not yet achieved the accurate three-dimensional reconstruction of objects smaller than 1 cm diameter. This paper describes the development of a novel volumetric method for small objects, using a binocular machine vision system. The achieved precision is high, providing a standard deviation of 0.04 mm. The robustness, of the system, issues from the lab prototype imaging system with the crucial z-axis movement without the need of further calibration and the fully automated volumetric algorithms.