DocumentCode :
1849716
Title :
Fully Automatic Liver Segmentation through Graph-Cut Technique
Author :
Massoptier, L. ; Casciaro, S.
Author_Institution :
Inst. of Clinical Physiol., Lecce
fYear :
2007
fDate :
22-26 Aug. 2007
Firstpage :
5243
Lastpage :
5246
Abstract :
The accurate knowledge of the liver structure including blood vessels topography, liver surface and lesion localizations is usually required in treatments like liver ablations and radiotherapy. In this paper, we propose an approach for automatic segmentation of liver complex geometries. It consists of applying a graph-cut method initialized by an adaptive threshold. The algorithm has been tested on 10 datasets (CT and MR). A parametric comparison with the results obtained by previous algorithms based on active contour is also carried out and discussed. Main limitations of active contour approaches result to be overcome and segmentation is improved. Feasibility to routinely use graph-cut approach for automatic liver segmentation is also demonstrated.
Keywords :
biomedical MRI; blood vessels; computerised tomography; image segmentation; liver; medical image processing; CT images; MR images; active contour; automatic segmentation; blood vessel topography; graph-cut technique; lesion localizations; liver; liver ablations; radiotherapy; Active contours; Biomedical imaging; Cancer; Computed tomography; Image segmentation; Liver neoplasms; Physiology; Surface morphology; Surface topography; Surgery; automatic segmentation; graph-cut; liver; Algorithms; Artificial Intelligence; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Liver; Liver Neoplasms; Magnetic Resonance Imaging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Tomography, X-Ray Computed;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
ISSN :
1557-170X
Print_ISBN :
978-1-4244-0787-3
Type :
conf
DOI :
10.1109/IEMBS.2007.4353524
Filename :
4353524
Link To Document :
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