Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Ruogu Fang , Ramin Zabih , Ashish Raj , Tsuhan Chen

ABSTRACT

We propose a novel approach that applies global optimal tree-metrics graph cuts algorithm on multi-phase contrast enhanced contrast enhanced MRI for liver tumor segmentation. To address the difficulties caused by low contrasted boundaries and high variability in liver tumor segmentation, we first extract a set of features in multi-phase contrast enhanced MRI data and use color-space mapping to reveal spatial-temporal information invisible in MRI intensity images. Then we apply efficient tree-metrics graph cut algorithm on multi-phase contrast enhanced MRI data to obtain global optimal labeling in an unsupervised framework. Finally we use tree-pruning method to reduce the number of available labels for liver tumor segmentation. Experiments on real-world clinical data show encouraging results. This approach can be applied to various medical imaging modalities and organs. More... »

PAGES

51-59

Book

TITLE

Abdominal Imaging. Computational and Clinical Applications

ISBN

978-3-642-28556-1
978-3-642-28557-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-28557-8_7

DOI

http://dx.doi.org/10.1007/978-3-642-28557-8_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013826549


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