Improving deformable surface meshes through omni-directional displacements and MRFs

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

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 227-234

  Kainmueller, D ^a   Lamecker, H ^a   Seim, H ^a   Zachow, S ^a   Hege, H C ^a  

^a ZUSE INSTITUTE BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONE BEAM; DEFORMABLE SURFACE MODELS; DEFORMABLE SURFACES; LINE SEGMENT; MARKOV RANDOM FIELDS; MESH CURVATURE; OMNI-DIRECTIONAL; SURFACE NORMALS; SYNTHETIC DATA; TARGET OBJECT; TRIANGULAR MESHES;

COMPUTERIZED TOMOGRAPHY; DEFORMATION; MEDICAL COMPUTING; MEDICAL IMAGING; SPHERES;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; CONE BEAM COMPUTED TOMOGRAPHY; FINITE ELEMENT ANALYSIS; HUMAN; IMAGE QUALITY; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; COMPUTER SIMULATION; CONE-BEAM COMPUTED TOMOGRAPHY; FINITE ELEMENT ANALYSIS; HUMANS; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883842288     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_28     Document Type: Conference Paper

References (10)

1. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Medical Image Analysis 1, 91-108 (1996)
2. Montagnat, J., Delingette, H., Ayache, N.: A Review of Deformable Surfaces: Topology, Geometry and Deformation. Image Vis. Comput. 19, 1023-1040 (2001)
3. Park, J.Y., McInerney, T., Terzopoulos, D., Kim, M.H.: A non-self-intersecting adaptive deformable surface for complex boundary extraction from volumetric images. Computers & Graphics 25(3), 421-440 (2001)
4. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models - Their Training and Application. Comput. Vis. Image Underst. 61(1), 38-59 (1995)
5. Kainmueller, D., Lange, T., Lamecker, H.: Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. In: 3D Segmentation in the Clinic: A Grand Challenge, pp. 109-116 (2007)
6. Komodakis, N., Tziritas, G., Paragios, N.: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primaldual strategies. Comput. Vis. Image Underst. 112(1), 14-29 (2008)
7. Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. MIA 12(6), 731-741 (2008); Special issue on information processing in medical imaging 2007
8. Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach. IEEE TPAMI 28(1), 119-134 (2006)
9. Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.C.: Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model. In: Proc. VCBM, pp. 93-100 (2008)
10. Kainmueller, D., Lamecker, H., Seim, H., Zinser, M., Zachow, S.: Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 76-83. Springer, Heidelberg (2009)