The Study Of OCT Imaging And Cellular Image Analysis

The cells are the building block of organisms. Their structure and function are of fundamental importance to many aspects of biological processes. A cell is not just a well-mixed container of chemicals, but an extremely intricate system of membranes, vesicles and signaling pathways. Although a number of milestones have been built though endeavor for over a century, much is still left to discover about the spatial organization and mobility of macro-molecules (such as proteins, protein complexes, nucleic acids, signaling substances, etc.) in cells, and the spatial relationships between cells in tissues. Understanding complex biological systems requires information and knowledge from many levels. Genomics and proteomics are recently popular and significant progresses have been made. Genomics provides important tools for studies of individuals on the genetic level, whereas proteomics explores structure, function and interactions of proteins. Although their discoveries provide tools and paths for the further understanding of cells, it is recognized that in many cases focusing only on gene and protein is inadequate; advancing more widely and deeply in the level of cell is necessary and imperative.In recent years, with the increasing demand of cell research, cellular imaging technologies develop rapidly. The trend is in ways: from observing whole cells to sub-cell structure, from observing statically to observing lively, from two-dimensional and single channel to three-dimensional and multiple channels, from in-vitro to in-vivo, as well as from no labeling to labeling. Among many imaging methods, we choose a novel one, Optical Coherence Tomography as our basis. The background, principle and advances of OCT are briefly introduced at first. Then, we describe the procedures of constructing and debugging our OCT system in details. Finally, a novel theoretical model named as PFMC is presented. It explains correctly the fact that the OCT signal decays exponentially penetrating into the sample.Whilst the observation of cells is routine with the help of varieties of imaging techniques, large sets of image data pose new challenges to the processing and analysis of them. Although the qualitative evaluation of cell images can be preformed visually, the manual interpretation of them is a tedious and time-consuming task. Moreover, irreproducible and subjective conclusions are usually drawn. Automatic image analysis is able to overcome these shortcomings. The thesis considers three topics: cell tracking in microscopic images, neurite labeling and Drosophila cell segmentation in fluorescence images. We propose two automated methods for tracking of cells in different shapes. One is based on dynamical Gaussian mixture model and the other is based on mathematical morphology. The neurite labeling is resolved by our novel multi-scale curvilinear structure detector. Two methods are utilized to segment the Drosophila cell images from RNAi experiments. They are based on Level set and Voronoi diagram, respectively. These approaches have the advantages of low cost, fast speed, reproducibility and objectivity. We hope they can serve as candidate tools for cell research.