Automatic ROI identification for fast liver tumor segmentation using graph-cuts

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7962, Issue , 2011, Pages

  Drechsler, Klaus ^a   Strosche, Michael ^a   Oyarzun Laura, Cristina ^a  

^a FRAUNHOFER IGD   (Germany)

Author keywords

Graph Cut; Reduction; ROI; Segmentation; Tumor

Indexed keywords

DATA SETS; FULL RESOLUTIONS; GRAPH-CUT; GROUND TRUTH; INTERACTIVE SEGMENTATION; LIVER TUMORS; LOW-INTENSITY; ORIGINAL IMAGES; REGION OF INTEREST; RELIABILITY AND ROBUSTNESS; RESOLUTION IMAGES; ROI; SEGMENTATION; TUMOR SEGMENTATION; UNDERLYING GRAPHS;

GRAPHIC METHODS; IMAGING SYSTEMS; MEDICAL IMAGING; TUMORS; USER INTERFACES;

IMAGE SEGMENTATION;

EID: 79958011880     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.878022     Document Type: Conference Paper

References (3)

1. Stawiaski, J., Decencire, E., and Bidault, F., "Interactive liver tumor segmentation using graph-cuts and watershed," in [MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge II. Liver Tumor Segmentation Challenge], (2008).
2. Lombaert, H., Sun, Y., Grady, L., and Xu, C., "A multilevel banded graph cuts method for fast image segmentation," in [Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV) ], 259-265, IEEE Computer Society, Washington, DC, USA (2005).
3. Boykov, Y. and Kolmogorov, V., "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision," IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124-1137 (2004).