Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

Medical Physics

Volumn 40, Issue 3, 2013, Pages

  Agner, Shannon C ^a   Xu, Jun ^b   Madabhushi, Anant ^c  

^a RUTGERS UNIVERSITY   (United States)

^b NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c Case Western Reserve University   (United States)

Author keywords

active contour; breast cancer; classification; computer aided diagnosis; DCE MRI; segmentation; spectral embedding

Indexed keywords

AUTOMATION; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DIMENSIONALITY REDUCTION; EIGENVALUES AND EIGENFUNCTIONS; EMBEDDINGS; KNOWLEDGE REPRESENTATION; MAGNETIC RESONANCE IMAGING; MAMMOGRAPHY; MEDICAL IMAGING; SUPPORT VECTOR MACHINES; TIME SERIES;

ACTIVE CONTOURS; AUTOMATED SEGMENTATION METHOD; BREAST CANCER; DCE-MRI; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; NONLINEAR DIMENSIONALITY REDUCTION; SPECTRAL EMBEDDING; SUPPORT VECTOR MACHINE CLASSIFIERS;

IMAGE SEGMENTATION;

ARTICLE; BREAST LESION; COMPUTER ASSISTED DIAGNOSIS; CONTRAST ENHANCEMENT; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; EMBEDDING; FUZZY SYSTEM; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SPECTRAL EMBEDDING BASED ACTIVE CONTOUR; SUPPORT VECTOR MACHINE; VOXEL BASED MORPHOMETRY; ALGORITHM; BREAST TUMOR; FUZZY LOGIC; IMAGE PROCESSING; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS;

CONTRAST MEDIUM; DIAGNOSTIC AGENT;

ALGORITHMS; BREAST NEOPLASMS; CONTRAST MEDIA; FUZZY LOGIC; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS;

EID: 84874765890     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4790466     Document Type: Article

References (48)

1. D. M. Ikeda, N. Hylton, K. Kinkel, M. G. Hochman, C. Kuhl, W. A. Kaiser, J. C. Weinreb, S. F. Smazal, H. Degani, P. Viehweg, J. Barclay, and M. D. Schnall, " Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging.," J. Magn. Reson. Imaging 13, 889-895 (2001). 10.1002/jmri.1127
2. M. D. Schnall, J. Blume, D. A. Bleumke, G. A. DeAngelis, N. DeBruhl, S. Harms, S. H. Heywang-Kobrunner, N. Hylton, C. Kuhl, E. D. Pisano, P. Causer, S. J. Schnitt, D. Thickman, C. B. Stelling, P. T. Weatherall, C. Lehman, and C. A. Gastonis, " Diagnositc architectural and dynamic features at breast MR imaging: Multicenter study.," Radiology 238 (1), 42-53 (2006). 10.1148/radiol.2381042117
3. K. Kinkel, T. H. Helbich, L. J. Esserman, J. Barclay, E. Schwerin, E. A. Sickles, and N. M. Hylton, " Dynamic high-spatial-resolution MR imaging of suspicious breast lesion: Diagnostic criteria and interobserver variability.," Am. J. Roentgenol. 175 (1), 35-43 (2000).
4. B. K. Szabo, P. Aspelin, and M. K. Wiberg, " Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: Comparison with empiric and quantitative kinetic parameters.," Acad. Radiol. 11, 1344-1354 (2004). 10.1016/j.acra.2004.09.006
5. T. Twellmann, A. Meyer-Baese, O. Lange, S. Foo, and T. W. Nattkemper, " Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning.," Eng. Applic. Artif. Intell. 21, 129-140 (2008). 10.1016/j.engappai.2007.04.005
6. W. Chen, M. L. Giger, U. Bick, and G. M. Newstead, " Automatic identification and classification of characteristic kinetic curves of breast lesion on DCE-MRI.," Med. Phys. 33 (8), 2878-2887 (2006). 10.1118/1.2210568
7. Q. Wu, M. Salganicoff, A. Krishnan, D. S. Fussell, and M. K. Markey, " Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model.," Proc. SPIE 6144, 61444M (2006). 10.1117/12.654308
8. J. Shi, B. Sahiner, H.-P. Chan, C. Paramagul, L. M. Hadjiiski, M. Helvie, and T. Chenevert, " Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation.," Med. Phys. 36 (11), 5052-5063 (2009). 10.1118/1.3238101
9. M. Kass, A. Witkin, and D. Terzopoulos, " Snakes: Active contour models.," Int. J. Comput. Vis. 1 (4), 321-331 (1988). 10.1007/BF00133570
10. V. Caselles, R. Kimmel, and G. Sapiro, " Geodesic active contours.," Int. J. Comput. Vis. 22, 61-79 (1997). 10.1023/A:1007979827043
11. T. F. Chan and L. A. Vese, " Active contours without edges.," IEEE Trans. Image Process. 10 (2), 266-277 (2001). 10.1109/83.902291
12. G. Sapiro, " Color snakes.," Comput. Vis. Image Underst. 68 (2), 247-253 (1997). 10.1006/cviu.1997.0562
13. T. F. Chan, B. Y. Sandberg, and L. A. Vese, " Active contours without edges for vector-valued images.," J. Visual Commun. Image Represent 11 (2), 130-141 (2000). 10.1006/jvci.1999.0442
14. M. Rousson and R. Deriche, " A variational framework for active and adaptative segmentation of vector valued images.," in Proceedings of the Workshop on Motion and Video Computing, 2002, Workshop occurred in Orlando, FL (IEEE Computer Society, Los Alamitos, CA, 2002), pp. 56-61.
15. J. Xu, A. Janowczyk, S. Chandran, and A. Madabhushi, " A high-throughput active contour scheme for segmentation of histopathological imagery.," Med. Image Anal. 15 (6), 851-862 (2011). 10.1016/j.media.2011. 04.002
16. W. G. Bradley and B. J. Glenn, " The effect of variation in slice thickness and interslice gap on MR lesion detection.," AJNR Am. J. Neuroradiol. 8 (6), 1057-1062 (1987).
17. G. H. Glover and N. J. Pelc, " Nonlinear partial volume artifacts in x-ray computed tomography.," Med. Phys. 7 (3), 238-248 (1980). 10.1118/1.594678
18. P. K. Saha and J. K. Udupa, " Scale-based diffusive image filtering preserving boundary sharpness and fine structures.," IEEE Trans. Med. Imaging 20 (11), 1140-1155 (2001). 10.1109/42.963817
19. L. G. Nyul, J. K. Udupa, and P. K. Saha, " Incorporating a measure of local scale in voxel-based 3-D image registration.," IEEE Trans. Med. Imaging 22 (2), 228-237 (2003). 10.1109/TMI.2002.808358
20. P. K. Saha, J. K. Udupa, and D. Odhner, " Scale-based fuzzy connected image segmentation: Theory, algorithms, and validation.," Comput. Vis. Image Underst. 77 (2), 145-174 (2000). 10.1006/cviu.1999.0813
21. P. K. Saha, " Tensor scale: A local morphometric parameter with applications to computer vision and image processing.," Comput. Vis. Image Underst. 99 (3), 384-413 (2005). 10.1016/j.cviu.2005.03.003
22. P. K. Saha, H. Zhang, J. K. Udupa, and J. C. Gee, " Tensor scale-based image registration.," Proc. SPIE 5032, 314-324 (2003). 10.1117/12.481433
23. A. Madabhushi, J. K. Udupa, and A. Souza, " Generalized scale: Theory, algorithms, and application to image inhomogeneity correction.," Comput. Vis. Image Underst. 101 (2), 100-121 (2006). 10.1016/j.cviu.2005.07.010
24. A. Souza, J. K. Udupa, and A. Madabhushi, " Image filtering via generalized scale.," Med. Image Anal. 12 (2), 87-98 (2008). 10.1016/j.media.2007.07.007
25. A. Madabhushi and J. K. Udupa, " New methods of MR image intensity standardization via generalized scale.," Med. Phys. 33 (9), 3426-3434 (2006). 10.1118/1.2335487
26. G. Hamarneh, C. McIntosh, and M. S. Drew, " Perception-based visualization of manifold-valued medical images using distance-preserving dimensionality reduction.," IEEE Trans. Med. Imaging 30 (7), 1314-1327 (2011). 10.1109/TMI.2011.2111422
27. E. Eyal, D. Badikhi, E. Furman-Haran, F. Kelcz, K. J. Kirshenbaum, and H. Degani, " Principal component analysis of breast DCE-MRI adjusted with a model-based method.," J. Magn. Reson Imaging 30 (5), 989-998 (2009). 10.1002/jmri.21950
28. A. R. Jamieson, M. L. Giger, K. Drukker, H. Li, Y. Yuan, and N. Bhooshan, " Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE.," Med. Phys. 37 (1), 339-351 (2010). 10.1118/1.3267037
29. G. Lee, C. Rodriguez, and A. Madabhushi, " Investigating the efficacy on nonlinear dimensionality reduction schemes in classifying gene and protein expression studies.," IEEE/ACM Trans. Comput. Biol. Bioinf. 5, 368-384 (2008). 10.1109/TCBB.2008.36
30. J. Shi and J. Malik, " Normalized cuts and image segmentation.," IEEE Trans. Pattern Anal. Mach. Intell. 22 (8), 888-905 (2000). 10.1109/34.868688
31. M. Meila and J. Shi, " A random walks view of spectral segmentation.," In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), Eighth International Workshop on Artificial Intelligence and Statistics. January 4-7, 2001, Key West, FL, 2001.
32. Y. Weiss, " Segmentation using eigenvectors: A unifying view.," IEEE Int. Conf. Comput. Vis. 2, 975-982 (1999). 10.1109/ICCV.1999.790354
33. U. von Luxburg, " A tutorial on spectral clustering.," Techinical Report No. TR-149 (Max Planck Institute for Biological Cybernetics, 2006).
34. A. Cumani, Edge Detection in Multispectral Images (Academic, New York, 1991), pp. 40-51.
35. S. Di Zenzo, A Note on the Gradient of a Multi-Image (Academic, San Diego, CA, 1986), Vol. 33, pp. 116-125.
36. L. D. Cohen, " On active contour models and balloons.," Comput. Vis. Graph. Image Process. 53 (2), 211-218 (1991). 10.1016/1049-9660(91)90028-N
37. C. Li, C. Xu, C. Gui, and M. D. Fox, " Level set evolution without re-initialization: A new variational formulation.," in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, Washington, DC, 2005), Vol. 1, pp. 430-436.
38. H. Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J. E. Tomaszewski, and A. Madabhushi, " Expectation maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology.," IEEE Trans. Biomed. Eng. 57 (7), 1676-1689 (2010). 10.1109/TBME.2010.2041232
39. C. Cortes and V. Vapnik, " Support-vector networks.," Mach. Learn. 20 (3), 273-297 (1995). 10.1007/BF00994018
40. S. C. Agner, J. Xu, M. Rosen, S. Karthigeyan, S. Englander, and A. Madabhushi, " Spectral embedding based active contour (SEAC): Application to breast lesion segmentation on DCE-MRI.," Proc. SPIE 7963, 796305 (2011). 10.1117/12.878218
41. K. Nie, J.-H. Chen, H. J. Yu, Y. Chu, O. Nalcioglu, and M.-Y. Su, " Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.," Acad. Radiol. 15 (12), 1513-1525 (2008). 10.1016/j.acra.2008.06.005
42. N. Bhooshan, M. L. Giger, S. A. Jansen, H. Li, L. Lan, and G. M. Newstead, " Cancerous breast lesions on dynamic contrast-enhanced MR images: Computerized characterization for image-based prognostic markers.," Radiology 254 (3), 680-690 (2010). 10.1148/radiol.09090838
43. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, 2nd ed. (Springer-Verlag, New York, 2002).
44. C. Rorden and M. Brett, " Stereotaxic display of brain lesions.," Behav. Neurol. 12 (4), 191-200 (2000).
45. S. C. Agner, S. Soman, E. Libfeld, M. McDonald, K. Thomas, S. Englander, M. A. Rosen, D. Chin, J. L. Nosher, and A. Madabhushi, " Textural kinetics: A novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.," J. Digit. Imaging 24 (3), 446-463 (2011). 10.1007/s10278-010-9298-1
46. K. G. A. Gilhuijs, M. L. Giger, and U. Bick, " Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging.," Med. Phys. 25 (9), 1647-1654 (1998). 10.1118/1.598345
47. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, " A nonparametric method for automatic correction of intensity nonuniformity in MRI data.," IEEE Trans. Med. Imaging 17 (1), 87-97 (1998). 10.1109/42.668698
48. H. J. Weber and G. B. Arfken, Mathematical Methods for Physicists (Academic, San Diego, 2003).