Projection onto the manifold of elongated structures for accurate extraction

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 316-324

  Sironi, Amos ^a   Lepetit, Vincent ^a,b   Fua, Pascal ^a  

^a EPFL   (Switzerland)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

2D IMAGES; DIFFERENT DOMAINS; ELONGATED STRUCTURES; GROUND TRUTH; NEAREST NEIGHBORS; SPATIAL CONSISTENCY; STATE-OF-THE-ART METHODS; TOPOLOGICAL ERRORS;

COMPUTER VISION;

EID: 84973863140     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.44     Document Type: Conference Paper

References (47)

1. G. Agam and C. Wu. Probabilistic Modeling-Based Vessel Enhancement in Thoracic CT Scans. In CVPR, 2005.
2. M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. Trans. Sig. Proc., 2006.
3. B. Andres, U. Koethe, M. Helmstaedter, W. Denk, and F. Hamprecht. Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. In DAGM, 2008.
4. B. Andres, T. Kröger, K. L. Briggman, W. Denk, N. Korogod, G. Knott, U. Köthe, and F. A. Hamprecht. Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV, 2012.
5. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011.
6. C. Becker, K. Ali, G. Knott, and P. Fua. Learning Context Cues for Synapse Segmentation. TMI, 2013.
7. C. Becker, R. Rigamonti, V. Lepetit, and P. Fua. Supervised Feature Learning for Curvilinear Structure Segmentation. In MICCAI, 2013.
8. J. Canny. A Computational Approach to Edge Detection. PAMI, 1986.
9. D. Ciresan, A. Giusti, L. Gambardella, and J. Schmidhuber. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. In NIPS, 2012.
10. A. Criminisi, P. Perez, and K. Toyama. Region Filling and Object Removal by Exemplar-Based Image Inpainting. TIP, 2004.
11. K. Dabov, A. Foi, and V. Katkovnik. Image Denoising by Sparse 3D Transformation-Domain Collaborative Filtering. JMLR, 2007.
12. P. Dollár and C. L. Zitnick. Fast Edge Detection Using Structured Forests. PAMI, 2015.
13. A. H. Foruzan, R. A. Zoroofi, Y. Sato, and M. Hori. A Hessian-Based Filter for Vascular Segmentation of Noisy Hepatic CT Scans. International Journal of Computer Assisted Radiology and Surgery, 2012.
14. A. Frangi, W. Niessen, K. Vincken, and M. Viergever. Multiscale Vessel Enhancement Filtering. Lecture Notes in Computer Science, 1998.
15. J. Funke, D. Andres, F. A. Hamprecht, A. Cardona, and M. Cook. Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data. CVPR, 2012.
16. Y. Ganin and V. Lempitsky. N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms. In ACCV, 2014.
17. G. Gonzalez, F. Aguet, F. Fleuret, M. Unser, and P. Fua. Steerable Features for Statistical 3D Dendrite Detection. In MICCAI, 2009.
18. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001.
19. V. Jain, J. Murray, F. Roth, S. Turaga, V. Zhigulin, K. Briggman, M. Helmstaedter, W. Denk, and H. Seung. Supervised Learning of Image Restoration with Convolutional Networks. In ICCV, 2007.
20. P. Kontschieder, S. Bulo, H. Bischof, and M. Pelillo. Structured Class-Labels in Random Forests for Semantic Image Labelling. In ICCV, 2011.
21. P. Kontschieder, S. R. Bulò, M. Donoser, M. Pelillo, and H. Bischof. Semantic Image Labelling as a Label Puzzle Game. In BMVC, 2011.
22. M. Law and A. Chung. Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux. In ECCV, 2008.
23. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-Based Learning Applied to Document Recognition. PIEEE, 1998.
24. J. Lim, C. L. Zitnick, and P. Dollár. Sketch Tokens: A Learned Mid-Level Representation for Contour and Object Detection. In CVPR, 2013.
25. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Non-Local Sparse Models for Image Restoration. In ICCV, 2009.
26. D. Marr and E. Hildreth. Theory of Edge Detection. Proceedings of the Royal Society of London, Biological Sciences, 1980.
27. A. Martinez-Sanchez, I. Garcia, and J. Fernandez. A Ridge-Based Framework for Segmentation of 3D Electron Microscopy Datasets. Journal of Structural Biology, 2013.
28. E. Meijering, M. Jacob, J.-C. F. Sarria, P. Steiner, H. Hirling, and M. Unser. Design and Validation of a Tool for Neurite Tracing and Analysis in Fluorescence Microscopy Images. Cytometry Part A, 2004.
29. H. Mirzaalian, T. Lee, and G. Hamarneh. Hair Enhancement in Dermoscopic Images Using Dual-Channel Quaternion Tubularness Filters and MRF-Based Multi-Label Optimization. TIP, 2014.
30. K. Mosaliganti, F. Janoos, A. Gelas, R. Noche, N. Obholzer, R. Machiraju, and S. Megason. Anisotropic Plate Diffusion Filtering for Detection of Cell Membranes in 3D Microscopy Images. In ICBI, 2010.
31. M. Muja and D. G. Lowe. Scalable Nearest Neighbor Algorithms for High Dimensional Data. PAMI, 2014.
32. W. Neuenschwander, P. Fua, G. Székely, and O. Kubler. Initializing Snakes. In CVPR, 1994.
33. M. Pechaud, G. Peyré, and R. Keriven. Extraction of Tubular Structures over an Orientation Domain. In CVPR, 2009.
34. X. Ren and L. Bo. Discriminatively Trained Sparse Code Gradients for Contour Detection. In NIPS, 2012.
35. J. Salmon and Y. Strozecki. Patch Reprojections for Non Local Methods. Signal Processing, 2012.
36. A. Santamará-Pang, T. Bildea, C. M. Colbert, P. Saggau, and I. Kakadiaris. Towards Segmentation of Irregular Tubular Structures in 3D Confocal Microscope Images. In MICCAI Workshop in Microscopic Image Analysis and Applications in Biology, 2006.
37. Y. Sato, C.-F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, and R. Kikinis. Tissue Classification Based on 3D Local Intensity Structure for Volume Rendering. IEEE Trans. on Visualization and Computer Graphics, 2000.
38. M. Seyedhosseini, M. Sajjadi, and T. Tasdizen. Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks. In ICCV, 2013.
39. A. Sironi, V. Lepetit, and P. Fua. Multiscale Centerline Detection by Learning a Scale-Space Distance Transform. In CVPR, 2014.
40. A. Sironi, E. Turetken, V. Lepetit, and P. Fua. Multiscale Centerline Detection. PAMI, In press 2015.
41. M. Sofka and C. Stewart. Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures. TMI, 2006.
42. J. Staal, M. Abramoff, M. Niemeijer, M. Viergever, and B. van Ginneken. Ridge Based Vessel Segmentation in Color Images of the Retina. TMI, 2004.
43. Z. Tu and X. Bai. Auto-Context and Its Applications to High-Level Vision Tasks and 3D Brain Image Segmentation. PAMI, 2009.
44. E. Turetken, C. Becker, P. Glowacki, F. Benmansour, and P. Fua. Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-Directional Oriented Flux. In ICCV, 2013.
45. A. Vazquez-Reina, M. Gelbart, D. Huang, J. Lichtman, E. Miller, and H. Pfister. Segmentation Fusion for Connectomics. In ICCV, 2011.
46. J. D. Wegner, J. A. Montoya-Zegarra, and K. Schindler. A Higher-Order CRF Model for Road Network Extraction. In CVPR, 2013.
47. Y. Zheng, M. Loziczonek, B. Georgescu, S. Zhou, F. Vega-Higuera, and D. Comaniciu. Machine Learning Based Vesselness Measurement for Coronary Artery Segmentation in Cardiac CT Volumes. SPIE, 2011.