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Conference papers
Segmentation of liver tumor via nonlocal active contours
Abstract : To reduce the manual labor time and provide the accuracy of liver tumor segmentation in the treatment planning of radiofrequency ablation (RFA), a novel method for liver tumor image segmentation by nonlocal active contours is proposed in this paper. A multi Gabor feature map of the liver tumor image is computed to describe the homogeneity of patches in a nonlocal way, and the nonlocal comparisons between pairs of patches are used to calculate the active contour energy. The whole energy function is minimized via a level set method to give the final segmentation. The experimental results indicate that the proposed method leads to good liver tumor segmentation with a good robustness to initialization condition. Experiment results show the proposed method can provide segmentation close to manual results, with the mean overlap error (OE) less than 23.86%
Keywords :
Active contours
Computed Tomography
error analysis
Gabor filters
Hidden Markov models
image segmentation
Liver
liver tumor segmentation
Manuals
mean overlap error
medical image processing
multi Gabor feature map
nonlocal active contours
patient treatment
radiofrequency ablation
RFA
treatment planning
tumors
tumours
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01320722
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, May 24, 2016 - 12:56:23 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, May 24, 2016 - 12:56:23 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM