Three - Dimensional Volume Reconstruction from Fluorescent

In this dissertation, I present a problem of three-dimensional volume reconstruction from fluorescent confocal laser scanning microscopy (CLSM) imagery. I overview a three-dimensional volume reconstruction framework which consists of (a) volume reconstruction procedures using multiple automation levels, feature types, and feature dimensionalities, (b) a data-driven registration decision support system, (c) an evaluation study of registration accuracy, and (d) a novel intensity enhancement technique for 3D CLSM volumes. The motivation for developing the framework came from the lack of 3D volume reconstruction techniques for CLSM image modality. The 3D volume reconstruction problem is challenging due to significant variations of intensity and shape of cross sectioned structures, unpredictable and inhomogeneous geometrical warping during medical specimen preparation, and an absence of external fiduciary markers. The framework addresses the problem of automation in the presence of the above challenges as they are frequently encountered during CLSM-based 3D volume reconstructions used for cell biology investigations. The objectives of the presented three-dimensional volume reconstruction framework are summarized as follows: (1) automate alignment of sub-volumes (physical sections) from multiple cross sections, (2) obtain high resolution image frames by mosaicking (i.e., stitching together), (3) quantify the accuracy of volume reconstruction using multiple techniques, and (4) visualize the reconstructed volumes in three-dimensional environments for visual inspection and quantitative interpretation. In this dissertation, the three-dimensional sub-volume registration problem is viewed primarily as an alignment problem. It is approached by extracting twoor three-dimensional