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Biomedical Engineering and Bioengineering Commons

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Theses/Dissertations

City University of New York (CUNY)

Bioimaging and Biomedical Optics

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Full-Text Articles in Biomedical Engineering and Bioengineering

Automatic Optimum Atlas Selection For Multi-Atlas Image Segmentation Using Joint Label Fusion, Kofi Agyeman Jan 2017

Automatic Optimum Atlas Selection For Multi-Atlas Image Segmentation Using Joint Label Fusion, Kofi Agyeman

Dissertations and Theses

Multi-atlas image segmentation using label fusion is one of the most accurate state of the art image segmentation techniques available for biomedical imaging applications. Motivated to achieve higher image segmentation accuracy, reduce computational costs and a continuously increasing atlas data size, a robust framework for optimum selection of atlases for label fusion is vital. Although believed not to be critical for weighted label fusion techniques by some works (Sabuncu, M. R. et al., 2010, [1]), others have shown that appropriate atlas selection has several merits and can improve multi-atlas image segmentation accuracy (Aljabar et al., 2009, [2], Van de Velde ...