Spectral embedding based active contour (SEAC): Application to breast lesion segmentation on DCE-MRI

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7963, Issue , 2011, Pages

  Agner, Shannon C ^a   Xu, Jun ^a   Rosen, Mark ^b   Karthigeyan, Sudha ^a   Englander, Sarah ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Active contour; breast cancer; classification; DCE MRI; magnetic resonance imaging; manifold learning; segmentation; spectral embedding

Indexed keywords

ACTIVE CONTOURS; BREAST CANCER; CLASSIFICATION; DCE-MRI; MANIFOLD LEARNING; SEGMENTATION; SPECTRAL EMBEDDING;

CLUSTERING ALGORITHMS; COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; EIGENVALUES AND EIGENFUNCTIONS; MEDICAL IMAGING; PIXELS; RESONANCE; TENSORS; TIME SERIES; VECTOR SPACES;

MAGNETIC RESONANCE IMAGING;

EID: 79955778399     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.878218     Document Type: Conference Paper

References (31)

1. J. Yezzi, A., S. Kichenassamy, A. Kumar, P. Olver, and A. Tannenbaum, "A geometric snake model for segmentation of medical imagery," Medical Imaging, IEEE Transactions on 16(2), pp. 199 -209, 1997. (Pubitemid 127762931)
2. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," International Journal of Computer Vision 22, pp. 61-79, 1997. 10.1023/A:1007979827043. (Pubitemid 127507390)
3. T. Chan and L. Vese, "Active contours without edges," Image Processing, IEEE Transactions on 10, pp. 266 -277, Feb. 2001. (Pubitemid 32254596)
4. N. Paragios and R. Deriche, "Coupled geodesic active regions for image segmentation: A level set approach," in Computer Vision ECCV 2000, D. Vernon, ed., Lecture Notes in Computer Science 1843, pp. 224-240, Springer Berlin / Heidelberg, 2000.
5. 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," Journal of Magnetic Resonance Imaging 13, pp. 889-895, 2001. (Pubitemid 32479947)
6. P. Saha and J. Udupa, "Scale-based diffusive image filtering preserving boundary sharpness and fine structures," Medical Imaging, IEEE Transactions on 20(11), pp. 1140 -1155, 2001. (Pubitemid 33106068)
7. 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, pp. 228-237, Feb 2003.
8. P. K. Saha, J. K. Udupa, and D. Odhner, "Scale-based fuzzy connected image segmentation: Theory, algorithms, and validation," Computer Vision and Image Understanding 77(2), pp. 145 - 174, 2000. (Pubitemid 30557365)
9. P. K. Saha, "Tensor scale: A local morphometric parameter with applications to computer vision and image processing," Computer Vision and Image Understanding 99(3), pp. 384 - 413, 2005. (Pubitemid 41162463)
10. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE PAMI 22(8), pp. 888-905, 2000.
11. 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, pp. 989-998, Nov 2009.
12. 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, pp. 339-351, Jan 2010.
13. G. Lee, C. Rodriguez, and A. Madabhushi, "Investigating the efficacy on nonlinear dimensionality reduction schemes in classifying gene and protein expression studies," IEEE/ACM Transactions on Computational Biology and Bioinformatics 5, pp. 368-384, 2008.
14. 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," Medical Physics 33(8), pp. 2878-2887, 2006. (Pubitemid 44154559)
15. 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," Academic Radiology 11, pp. 1344-1354, 2004. (Pubitemid 39642821)
16. 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), pp. 42-53, 2006. (Pubitemid 43010579)
17. 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), pp. 35-43, 2000. (Pubitemid 30415534)
18. 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. Appl. Artif. Intell. 21, pp. 129-140, 2008. (Pubitemid 351381572)
19. 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," Medical Imaging 2006: Image Processing 6144(1), p. 61444M, SPIE, 2006.
20. 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," Medical Physics 36(11), pp. 5052-5063, 2009.
21. G. Sapiro, "Color snakes," CVIU 68(2), pp. 247-253, 1997. (Pubitemid 127435828)
22. T. F. Chan, B. Y. Sandberg, and L. A. Vese, "Active contours without edges for vector-valued images," Journal of Visual Communication and Image Representation 11(2), pp. 130 - 141, 2000.
23. M. Rousson and R. Deriche, "A variational framework for active and adaptative segmentation of vector valued images," in Motion and Video Computing, 2002. Proceedings. Workshop on, pp. 56 - 61, 2002.
24. J. Xu, A. Janowczyk, S. Chandran, and A. Madabhushi, "A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation," Medical Imaging 2010: Image Processing 7623(1), p. 76230Y, SPIE, 2010.
25. C. Aldo, "Edge detection in multispectral images," pp. 40-51, Academic Press, Inc., 1991.
26. S. Di Zenzo, "A note on the gradient of a multi-image," 33(1), pp. 116-125, Academic Press Professional, Inc., San Diego, CA, USA, 1986.
27. C. Rorden and M. Brett, "Stereotaxic display of brain lesions.," Behav Neurol 12(4), pp. 191-200, 2000. (Pubitemid 32843166)
28. 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," Academic Radiology 15(12), pp. 1513 - 1525, 2008.
29. 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), pp. 680-90, 2010.
30. S. Agner, S. Soman, E. Libfeld, M. McDonald, K. Thomas, S. Englander, M. Rosen, D. Chin, J. Nosher, and A. Madabhushi, "Textural kinetics: A novel dynamic contrast-enhanced (dce)-mri feature for breast lesion classification," Journal of Digital Imaging, pp. 1-18, 2010. 10.1007/s10278-010-9298-1.
31. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning 20(3), pp. 273-297, 1995.