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Slice Based 3D Cell Segmentation of Optical Projection Tomography Images

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dc.contributor.advisor Jari, Hyttinen (Prof.)
dc.contributor.author Haile, Baye
dc.date.accessioned 2020-03-12T10:14:32Z
dc.date.available 2020-03-12T10:14:32Z
dc.date.issued 2018-01
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/21125
dc.description.abstract Optical Projection Tomography (OPT) is a new imaging technique used to image cells in 3D cultured in a hydrogel. Reconstructed OPT images of cells suffer from several artifacts. These artifacts reduce the overall image quality. This will create a challenge for isolating and studying each cell in 3D cell culture. It is highly required to enhance the 3D OPT images of cells for successful analysis of cell interaction and growth in 3D cell culture. In that regard, this thesis intends to build a robust algorithm for use in effective segmentation of cells cultured inside hydrogel. There exist various 3D cell segmentation algorithms in the literature including those schemes that rely on thresholding, edge detection, region growing and clustering approaches. Among these algorithms, moving average adaptive thresholding (MAAT) and region growing algorithm present commendable performance in segmentation of cells identified on OPT images. In this thesis the performance of an automatic seeded region growing algorithm (ASRGA) and MAAT have been compared rigorously in terms of their use in 2D slice based segmentation of the cells on the 3D OPT image sets considered. Results have shown that the MAAT method show superior performance and provide promising 3D visualization of cells. The output of the research will have a tremendous contribution to reduce artifacts in 3D cell images and enhance 3D visualization. en_US
dc.language.iso en_US en_US
dc.publisher Addis Ababa University en_US
dc.subject Optical Projection Tomography en_US
dc.subject hydrogel en_US
dc.subject cell segmentation en_US
dc.subject region growing algorithm en_US
dc.subject moving average en_US
dc.title Slice Based 3D Cell Segmentation of Optical Projection Tomography Images en_US
dc.type Thesis en_US


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