Nugget-cut: A segmentation scheme for spherically- and elliptically-shaped 3D objects

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6376 LNCS, Issue , 2010, Pages 373-382

  Egger, Jan ^a   Bauer, Miriam H A ^a   Kuhnt, Daniela ^a   Carl, Barbara ^a   Kappus, Christoph ^a   Freisleben, Bernd ^a   Nimsky, Christopher ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

glioma; graph; minimal s t cut; polyhedra; segmentation

Indexed keywords

GLIOMA; GRAPH; MINIMAL S-T CUT; POLYHEDRA; SEGMENTATION;

GEOMETRY; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION; POLYNOMIAL APPROXIMATION; RESONANCE;

THREE DIMENSIONAL;

EID: 78349244875     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15986-2_38     Document Type: Conference Paper

References (27)

1. Kleihues, P., et al.: The WHO classification of tumors of the nervous system. Journal of Neuropathology & Experimental Neurology 61(3), 215-229 (2002)
2. Kortmann, R.D., et al.: Radiochemotherapy of malignant gliom in adults. Clinical experiences. Strahlenther Onkol. 179(4), 219-232 (2003)
3. Lacroix, M., Abi-Said, D., Fourney, D.R., et al.: A multivariate analysis of 416 patients with glioblastoma multiforme: Prognosis, extent of resection and survival. Journal of Neurosurgery 95, 190-198 (2001)
4. Angelini, E.D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications. Current Medical Imaging Reviews 3, 262-276 (2007)
5. Gibbs, P., Buckley, D.L., Blackband, S.J., et al.: Tumour volume determination from MR images by morphological segmentation. Physics in Med. & Biology 41(11), 2437-2446 (1996) (Pubitemid 26376914)
6. Letteboer, M.M.J., Olsen, O.F., Dam, E.B., et al.: Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Academic Radiology 11, 1125-1138 (2004)
7. Droske, M., Meyer, B., Rumpf, M., et al.: An adaptive level set method for interactive segmentation of intracranial tumors. Neurol. Res. 27(4), 363-370 (2005)
8. Clark, M., Hall, L.O., Goldgof, D.B., et al.: Automatic tumor segmentation using knowledge-based techniques. IEEE TMI 17(2), 187-201 (1998) (Pubitemid 128747672)
9. Prastawa, M., Bullitt, E., Ho, S., et al.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis 8, 275-283 (2004) (Pubitemid 39185911)
10. Sieg, C., Handels, H., Pöppl, S.J.: Automatic Segmentation of Contrast-Enhanced Brain Tumors in Multispectral MR-Images with Backpropagation-Networks. In: Bildverarbeitung für die Medizin (BVM), pp. 347-351. Springer Press, Heidelberg (2001) (in German)
11. Egger, J., Mostarkic, Z., Großkopf, S., Freisleben, B.: A Fast Vessel Centerline Extraction Algorithm for Catheter Simulation. In: 20th IEEE International Symposium on Computer-Based Medical Systems, Maribor, Slovenia, pp. 177-182. IEEE Press, Los Alamitos (2007)
12. Egger, J., O'Donnell, T., Hopfgartner, C., Freisleben, B.: Graph-Based Tracking Method for Aortic Thrombus Segmentation. In: Proceedings of 4th European Congress for Medical and Biomedical Engineering, Engineering for Health, Antwerp, Belgium (2008)
13. Egger, J., Freisleben, B., Setser, R., Renapuraar, R., Biermann, C., O'Donnell, T.: Aorta Segmentation for Stent Simulation. In: 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Cardiovascular Interventional Imaging and Biophysical Modelling Workshop, London, United Kingdom, 10pages (2009)
14. Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 119-134 (2006) (Pubitemid 46395309)
15. Bauer, M.H.A., Egger, J., O'Donnell, T., Freisleben, B., Barbieri, S., Klein, J., Hahn, H.K., Nimsky, C.: A Fast and Robust Graph-based Approach for Boundary Estimation of Fiber Bundles Relying on Fractional Anisotropy Maps. In: 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey. IEEE Computer Society, Los Alamitos (2010)
16. Veksler, O.: Star Shape Prior for Graph-Cut Image Segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 454-467. Springer, Heidelberg (2008)
17. Ishikawa, H., Geiger, D.: Segmentation by Grouping Junctions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 125-131 (1998)
18. Grady, L., Schwartz, E.L.: Isoperimetric Graph Partitioning for Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(3), 469-475 (2006) (Pubitemid 43159642)
19. Boykov, Y., 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)
20. Shabou, A., et al.: A graph-cut based algorithm for approximate MRF optimization. In: International Conference on Image Processing (ICIP), pp. 2413-2416 (2009)
21. Kass, M., Witkin, A., Terzopoulos, D.: Constraints on Deformable Models: Recovering 3D Shape and Nongrid Motion. Artificial Intelligence 36, 91-123 (1988)
22. MeVisLab - development environment for medical image processing and visualization. MeVis Medical Solutions AG and Fraunhofer MEVIS, Bremen, Germany, http://www.mevislab.de
23. Zou, K.H., Warfield, S.K., Bharatha, A., et al.: Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index: Scientific Reports. Academic Radiology 11(2), 178-189 (2004)
24. Egger, J., Bauer, M.H.A., Kuhnt, D., Kappus, C., Carl, B., Freisleben, B., Nimsky, C.: A Flexible Semi-Automatic Approach for Glioblastoma multiforme Segmentation. In: Biosignal Processing Conference, DGBMT, IEEE, Charité, Berlin, Germany (2010)
25. Greiner, K., et al.: Segmentation of Aortic Aneurysms in CTA-images with the Statistic Method of the Active Appearance Models. In: Bildverarbeitung für die Medizin (BVM), pp. 51-55. Springer Press, Heidelberg (2008) (in German)
26. Egger, J., Bauer, M.H.A., Kuhnt, D., Freisleben, B., Nimsky, C.: Pituitary Adenoma Segmentation. In: Proceedings of International Biosignal Processing Conference, DGBMT, IEEE, Charité, Berlin, Germany (2010)
27. Ma, B., Harbaugh, R.E., Raghavan, M.L.: Three-Dimensional Geometrical Characterization of Cerebral Aneurysms. Ann. of Biomed. Eng. 32(2), 264-273 (2004)