Advanced image segmentation and data clustering concepts applied to digital image sequences featuring the response of biological materials to toxic agents Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j3860975b

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  • Image segmentation is the process by which an image is divided into number of regions. The regions are to be homogeneous with respect to some property. Definition of homogeneity depends mainly on the expected patterns of the objects of interest. The algorithms designed to perform these tasks can be divided into two main families: Splitting Algorithms and Merging Algorithms. The latter comprises seeded region growing algorithms which provide the basis for our work. Seeded region growing methods such as Marker initiated Watershed segmentation depend principally on the quality and relevance of the initial seeds. In situations where the image contains a variety of aggregated objects of different shapes, finding reliable initial seeds can be a very complex task. This thesis describes a versatile approach for finding initial seeds on images featuring objects distinguishable by their structural and intensity profiles. This approach involves the use of hierarchical trees containing various information on the objects in the image. These trees can be searched for specific pattern to generate the initial seeds required to perform a reliable region growing process. Segmentation results are shown in this thesis. The above image segmentation scheme has been applied to detect isolated living cells in a sequence of frames and monitor their behavior through the time. The tissues utilized for these studies are isolated from the scales of Betta Splendens fish family. Since the isolated cells or chromatophores are sensitive to various kinds of toxic agents, a creation of cell-based toxin detector was suggested. Such sensor operation depends on an efficient segmentation of cell images and extraction of pertinent visual features. Our ultimate objective is to model and classify the observed cell behavior in order to detect and recognize biological or chemical agents affecting the cells. Some possible modelling and classification approaches are presented in this thesis.
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