A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images |
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Authors: | Oliver Gloger,Jens Kü hn,Adam Stanski,Henry Vö lzke,Ralf Puls |
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Affiliation: | 1. Ernst Moritz Arndt University of Greifswald, Institute for Community Medicine, Study of Health in Pomerania (SHIP), 17489 Greifswald, Germany;2. Ernst Moritz Arndt University of Greifswald, Institut für Diagnostische Radiologie und Neuroradiologie, Ferdinand-Sauerbruch-Straβe, 17489 Greifswald, Germany;3. Berlin University of Technology (TUB), Computer Vision and Remote Sensing, Franklinstraβe 28/29, 10587 Berlin, Germany |
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Abstract: | Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties. |
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