Three dimensional reconstruction of the cell cytoskeleton from stereo images

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (leaves 80-83).Besides its primary application to robot vision, stereo vision also appears promising in the biomedical field. This study examines 3D reconstruction of the cell cytoskeleton. This application of stereo vision to electron micrographs extracts information about the interior structure of cells at the nanometer scale level. We propose two different types of stereo vision approaches: the line-segment and wavelet multiresolution methods. The former is primitive-based and the latter is a point-based approach. Structural information is stressed in both methods. Directional representation is employed to provide an ideal description for filament-type structures. In the line-segment method, line-segments are first extracted from directional representation and then matching is conducted between two line-segment sets of stereo images. A new search algorithm, matrix matching, is proposed to determine the matching globally. In the wavelet multiresolution method, a pyramidal architecture is presented. Bottom-up analysis is first performed to form two pyramids, containing wavelet decompositions and directional representations. Subsequently, top-down matching is carried out. Matching at a high level provides guidance and constraints to the matching at a lower level. Our reconstructed results reveal 3D structure and the relationships of filaments which are otherwise hard to see in the original stereo images. The method is sufficiently robust and accurate to allow the automated analysis of cell structural characteristics from electron microscopy pairs. The method may also have application to a general class of stereo images.by Yuan Cheng.S.M

Three-dimensional reconstruction from two-dimensional images and applications to cell cytoskeleton

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2001.Includes bibliographical references (leaves 121-129).Approaches to achieve three dimensional (3D) reconstruction from 2D images can be grouped into two categories: computer-vision-based reconstruction and tomographic reconstruction. By exploring both the differences and connections between these two types of reconstruction, the thesis attempts to develop a new technique that can be applied to 3D reconstruction of biological structures. Specific attention is given to the reconstruction of the cell cytoskeleton from electron microscope images. The thesis is composed of two parts. The first part studies computer-vision-based reconstruction methods that extract 3D information from geometric relationship among images. First, a multiple-feature-based stereo reconstruction algorithm that recovers the 3D structure of an object from two images is presented. A volumetric reconstruction method is then developed by extending the algorithm to multiple images. The method integrates a sequence of 3D reconstruction from different stereo pairs. It achieves a globally optimized reconstruction by evaluating certainty values of each stereo reconstruction. This method is tuned and applied to 3D reconstruction of the cell cytoskeleton. Feasibility, reliability and flexibility of the method are explored.(cont.) The second part of the thesis focuses on a special tomographic reconstruction, discrete tomography, where the object to be reconstructed is composed of a discrete set of materials each with uniform values. A Bayesian labeling process is proposed as a framework for discrete tomography. The process uses an expectation-maximization (EM) algorithm with which the reconstruction is obtained efficiently. Results demonstrate that the proposed algorithm achieves high reconstruction quality even with a small number of projections. An interesting relationship between discrete tomography and conventional tomography is also derived, showing that discrete tomography is a more generalized form of tomography and conventional tomography is only a special case of such generalization.by Yuan Cheng.Ph.D