Images as occlusions of textures: A framework for segmentation

IEEE Transactions on Image Processing

Volumn 23, Issue 5, 2014, Pages 2033-2046

  McCann, Michael T ^a   Mixon, Dustin G ^b   Fickus, Matthew C ^b   Castro, Carlos A ^c   Ozolek, John A ^d   Kovacevic, Jelena ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Air Force Institute of Technology   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

^d CHILDREN S HOSPITAL OF PITTSBURGH   (United States)

Author keywords

deconvolution; image segmentation; local histograms; non negative matrix factorization; occlusion models; texture

Indexed keywords

DECONVOLUTION; GRAPHIC METHODS; IMAGE TEXTURE; TEXTURES;

ALGORITHMIC FRAMEWORK; IMAGE DE CONVOLUTIONS; IMAGE PROCESSING APPLICATIONS; LOCAL HISTOGRAM; NONNEGATIVE MATRIX FACTORIZATION; OCCLUSION MODEL; SEGMENTATION METHODS; UNSUPERVISED IMAGE SEGMENTATION;

IMAGE SEGMENTATION;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; MICROSCOPY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84898037550     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2307475     Document Type: Article

References (48)

1. D. R. Li, G. Zhang, Z. Wu, and L. Yi, "An edge embedded markerbased watershed algorithm for high spatial resolution remote sensing image segmentation," IEEE Trans. Image Process., vol. 19, no. 10, pp. 2781-2787, Oct. 2010.
2. N. Jacobson, Y.-L. Lee, V. Mahadevan, N. M. Vasconcelos, and T. Q. Nguyen, "A novel approach to FRUC using discriminant saliency and frame segmentation," IEEE Trans. Image Process., vol. 19, no. 11, pp. 2924-2934, Nov. 2010.
3. H. Ayasso and A. Mohammad-Djafari, "Joint NDT image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation," IEEE Trans. Image Process., vol. 19, no. 9, pp. 2265-2277, Sep. 2010.
4. A. Gooya, H. Liao, K. Matsumiya, K. Masamune, Y. Masutani, and T. Dohi, "A variational method for geometric regularization of vascular segmentation in medical images," IEEE Trans. Image Process., vol. 17, no. 8, pp. 1295-1312, Aug. 2008.
5. C. Zanella, M. Campana, B. Rizzi, C. Melani, G. Sanguinetti, P. Bourgine, et al., "Cells segmentation from 3-D confocal images of early zebrafish embryogenesis," IEEE Trans. Image Process., vol. 19, no. 3, pp. 770-781, Mar. 2010.
6. G. Srinivasa, M. C. Fickus, Y. Guo, A. D. Linstedt, and J. Kova?cevíc, "Active mask segmentation of fluorescence microscope images," IEEE Trans. Image Process., vol. 18, no. 8, pp. 1817-1829, Aug. 2009.
7. K.-C. Chen, Y. Yu, R. Li, H.-C. Lee, G. Yang, and J. Kova?cevíc, "Adaptive active-mask image segmentation for quantitative characterization of mitochondrial morphology," in Proc. IEEE Int. Conf. Image Process., Orlando, FL, USA, Sep. 2012, pp. 2033-2036.
8. D. B. Chklovskii, S. Vitaladevuni, and L. K. Scheffer, "Semi-automated reconstruction of neural circuits using electron microscopy," Current Opin. Neurobiol., vol. 20, no. 5, pp. 667-675, Oct. 2010.
9. J. Nascimento and J. Marques, "Robust shape tracking with multiple models in ultrasound images," IEEE Trans. Image Process., vol. 17, no. 3, pp. 392-406, Mar. 2008. (Pubitemid 351386298)
10. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
11. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," Int. J. Comput. Vis., vol. 1, no. 4, pp. 321-331, 1988.
12. R. Malladi, J. A. Sethian, and B. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 2, pp. 158-175, Feb. 1995.
13. A. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern Recognit., vol. 24, no. 12, pp. 1167-1186, 1991.
14. H. Derin and W. S. Cole, "Segmentation of textured images using Gibbs random fields," Comput. Vis. Graph. Image Process., vol. 35, no. 1, pp. 72-98, Jul. 1986.
15. L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 6, pp. 583-598, Jun. 1991. (Pubitemid 21675616)
16. R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 6, pp. 641-647, Jun. 1994.
17. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002. (Pubitemid 34550429)
18. C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image segmentation using expectation-maximization and its application to image querying," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 8, pp. 1026-1038, Aug. 1999.
19. Y. Deng and B. S. Manjunath, "Unsupervised segmentation of colortexture regions in images and video," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 8, pp. 800-810, Aug. 2001. (Pubitemid 32780009)
20. C. M. Christoudias, B. Georgescu, and P. Meer, "Synergism in low level vision," in Proc. IEEE Int. Conf. Pattern Recognit., vol. 4. Quebec, QC, Canada, Aug. 2002, pp. 150-155. (Pubitemid 44850969)
21. D. E. Ilea and P. F. Whelan, "CTex-An adaptive unsupervised segmentation algorithm based on color-texture coherence," IEEE Trans. Image Process., vol. 17, no. 10, pp. 1926-1939, Oct. 2008.
22. T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, "Use of active shape models for locating structures in medical images," Image Vis. Comput., vol. 12, no. 6, pp. 355-365, Jul. 1994.
23. J. A. Ozolek and C. A. Castro, Teratomas Derived from Embryonic Stem Cells as Models for Embryonic Development, Disease, and Tumorigenesis. Vellore, India: InTech., 2011, ch. 13.
24. C. Chen, J. A. Ozolek, W. Wang, and G. K. Rohde, "A general system for automatic biomedical image segmentation using intensity neighborhoods," Int. J. Biomed. Imag., vol. 2011, pp. 606857-1-606857-12, Jan. 2011, doi:10.1155/2011/606857
25. A. Cagri, A. Burak, C. Aykanat, C. Sokmensuer, and C. Gunduz-Demir, "Multilevel segmentation of histopathological images using cooccurrence of tissue objects," IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1681-1690, Jun. 2012.
26. M. L. Massar, R. Bhagavatula, M. Fickus, and J. Kova?cevíc, "Local histograms and image occlusion models," Appl. Comput. Harmon. Anal., vol. 34, no. 3, pp. 469-487, May 2013.
27. A. B. Lee, D. Mumford, and J. Huang, "Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model," Int. J. Comput. Vis., vol. 41, nos. 1-2, pp. 35-59, 2001. (Pubitemid 32512205)
28. M. Haindl and S. Mikes, "Texture segmentation benchmark," in Proc. IEEE Int. Conf. Pattern Recogn., Dec. 2008, pp. 1-4.
29. O. Michailovich, Y. Rathi, and A. Tannenbaum, "Image segmentation using active contours driven by the bhattacharyya gradient flow," IEEE Trans. Image Process., vol. 16, no. 11, pp. 2787-2801, Nov. 2007. (Pubitemid 350141851)
30. R. Courant, "Variational methods for the solution of problems of equilibrium and vibrations," Bull. Amer. Math. Soc., vol. 49, no. 1, pp. 1-24, Jan. 1943.
31. M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, "Fast image recovery using variable splitting and constrained optimization," IEEE Trans. Image Process., vol. 19, no. 9, pp. 2345-2356, Sep. 2010.
32. M. L. Fisher, "An applications oriented guide to lagrangian relaxation," Interfaces, vol. 15, no. 2, pp. 10-21, Mar. 1985.
33. D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, Oct. 1999.
34. M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons, "Algorithms and applications for approximate nonnegative matrix factorization," Comput. Statist. Data Anal., vol. 52, no. 1, pp. 155-173, Sep. 2007. (Pubitemid 47331703)
35. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Englewood Cliffs, NJ, USA: Prentice-Hall, 2002.
36. P. Sarder and A. Nehorai, "Deconvolution methods for 3-D fluorescence microscopy images," IEEE Signal Process. Magn., vol. 23, no. 3, pp. 32-45, May 2006.
37. P. Paatero and U. Tapper, "Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values," Environmetrics, vol. 5, no. 2, pp. 111-126, Jun. 1994.
38. L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Phys. D, vol. 60, nos. 1-4, pp. 259-268, Nov. 1992.
39. A. K. Jain, "Data clustering: 50 years beyond k-means," Pattern Recognit. Lett., vol. 31, no. 8, pp. 651-666, Jun. 2010.
40. M. T. McCann, D. G. Mixon, M. Fickus, C. A. Castro, J. A. Ozolek, and J. Kova?cevíc. (2013). Images as Occlusions of Textures: A Framework for Segmentation [Online]. Available: http://www.jelena.ece. cmu.edu/repository/rr/ 13-McCannMFCOK/13-McCannMFC%OK.html
41. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "Contour detection and hierarchical image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898-916, May 2011.
42. P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vis., vol. 59, no. 2, pp. 167-181, 2004.
43. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. 8th Int. Conf. Comput. Vis., vol. 2. Jul. 2001, pp. 416-423. (Pubitemid 32795086)
44. W. M. Rand, "Objective criteria for the evaluation of clustering methods," J. AmER. Statist. Assoc., vol. 66, no. 336, pp. 846-850, Dec. 1971.
45. R. Unnikrishnan, C. Pantofaru, and M. H. Hebert, "Toward objective evaluation of image segmentation algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 929-944, Jun. 2007. (Pubitemid 46667408)
46. D. E. Ilea and P. F. Whelan, "Image segmentation based on the integration of colour-texture descriptors-A review," Pattern Recogn., vol. 44, nos. 10-11, pp. 2479-2501, Oct. 2011.
47. M. Meila, "Comparing clusterings by the variation of information," in Learning Theory and Kernel Machines (Lecture Notes in Computer Science), B. Schölkopf and M. Warmuth, Eds. Berlin, Germany: Springer-Verlag, 2003, pp. 173-187. (Pubitemid 37053203)
48. A. Dvoretzky, J. Kiefer, and J. Wolfowitz, "Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator," Ann. Math. Statist., vol. 27, no. 3, pp. 642-669, Sep. 1956.