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Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationMedical Imaging 2010
Subtitle of host publicationImage Processing
EditorsBenoit M. Dawant, David R. Haynor
Place of PublicationBellingham
PublisherSPIE-International Society for Optical Engineering
Number of pages9
ISBN (Print)9780819480248
StatePublished - 2010
EventSPIE Medical Imaging 2010: Image Processing - San Diego, United States

Publication series

NameProgress in Biomedical Optics and Imaging
NameProceedings of SPIE


ConferenceSPIE Medical Imaging 2010: Image Processing
CountryUnited States
CitySan Diego
Internet address


In this study, clinically produced multiphase CT volumetric data sets (pre-contrast, arterial and venous enhanced phase) are drawn upon to transcend the intrinsic limitations of single phase data sets for the robust and accurate segmentation of the liver in typically challenging cases. As an initial step, all other phase volumes are registered to either the arterial or venous phase volume by a symmetric nonlinear registration method using mutual information as similarity metric. Once registered, the multiphase CT volumes are pre-filtered to prepare for subsequent steps. Under the assumption that the intensity vectors of different organs follow the Gaussian Mixture model (GMM), expectation maximization (EM) is then used to classify the multiphase voxels into different clusters. The clusters for liver parenchyma, vessels and tumors are combined together and provide the initial liver mask that is used to generate initial zeros level set. Conversely, the voxels classified as non-liver will guide the speed image of the level sets in order to reduce leakage. Geodesic active contour level set using the gradient vector flow (GVF) derived from one of the enhanced phase volumes is then performed to further evolve the liver segmentation mask. Using EM clusters as the reference, the resulting liver mask is finally morphologically post-processed to add missing clusters and reduce leakage. The proposed method has been tested on the clinical data sets of ten patients with relatively complex and/or extensive liver cancer or metastases. A 95.8% dice similarity index when compared to expert manual segmentation demonstrates the high performance and the robustness of our proposed method - even for challenging cancer data sets - and confirms the potential of a more thorough computational exploitation of currently available clinical data sets.



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