A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images

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 imag...

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Vydáno v:Magnetic resonance imaging Ročník 28; číslo 6; s. 882 - 897
Hlavní autoři: Gloger, Oliver, Kühn, Jens, Stanski, Adam, Völzke, Henry, Puls, Ralf
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier Inc 01.07.2010
Témata:
Adult
Aged
Algorithms
Automatic liver segmentation
Bayesian formulation
Discriminant Analysis
Fatty Liver - pathology
Female
Fourier Analysis
Fourier descriptors
Humans
Image Enhancement - methods
Imaging, Three-Dimensional - methods
Kidney - anatomy & histology
Liver - anatomy & histology
Liver - pathology
Magnetic Resonance Imaging - methods
Male
Middle Aged
Multiclass linear dimension reduction
Radiology
Region growing
Young Adult
Automatic liver segmentation
Bayesian formulation
Multiclass linear dimension reduction
Region growing
Fourier descriptors
ISSN:0730-725X, 1873-5894, 1873-5894
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Shrnutí: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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ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2010.03.010