Pop out many small structures from a very large microscopic image

Medical Image Analysis

Volumn 15, Issue 5, 2011, Pages 690-707

  Bernardis, Elena ^a   Yu, Stella X ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b BOSTON COLLEGE   (United States)

Author keywords

Digital pathology; Segmentation; Spectral graph partitioning

Indexed keywords

DIGITAL PATHOLOGY; FINE STRUCTURES; LARGE IMAGES; MEDICAL RESEARCH; MICROSCOPIC IMAGE; PATCH SEGMENTATION; SMALL REGION; SPECTRAL GRAPH PARTITIONING;

GRAPH THEORY; MEDICAL IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; CONCEPTUAL FRAMEWORK; CONSTRAINED SEGMENTATION; DIGITAL IMAGING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MICROSCOPE IMAGE; MOLECULAR IMAGING; MOLECULAR SIZE; PATCH SEGMENTATION; PRIORITY JOURNAL; QUALITY CONTROL;

ALGORITHMS; ANIMALS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; LYMPHOCYTES; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED;

EID: 80052166641     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2011.06.009     Document Type: Article

References (36)

1. Barash D., Comaniciu D. A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image Vision Computing 2004, 22:73-81.
2. Barbu, A., Zhu, S.C., 2003. Graph partition by swendsen-wang cuts. In: Proceedings of the Ninth IEEE Computer Society Conference on International Conference of Computer Vision (ICCV 2003), vol. 2, pp. 320-327.
3. Basavanhally A., Ganesan S., Agner S., Monaco J.P., Feldman M.D., Tomaszewski J.E., Bhanot G., Madabhushi A. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Transactions Biomedical Engineering 2010, 57:3, 642-5.
4. Bernardis E., Yu S.X. Finding dots: segmentation as popping out regions from boundaries. Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010) 2010, 199-206. IEEE Computer Society.
5. Bernardis E., Yu S.X. Segmentation subject to stitching constraints: finding many small structures in a large image. Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I (MICCAI 2010) 2010, 119-126. Springer-Verlag, Berlin, Heidelberg.
6. Cheng J., Veronika M., Rajapakse J. Identifying cells in histopathological images. Proceedings of the ICPR 2010 Contests 2010, Springer.
7. Couprie, C., Grady, L., Najman, L., Talbot, H., 2009. Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In: Proceedings of the 12th International Conference of Computer Vision (ICCV 2009), pp. 731-738.
8. Cour T., Benezit F., Shi J. Spectral segmentation with multiscale graph decomposition. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) 2005, 1124-1131. IEEE Computer Society, Washington, DC, USA.
9. Cousty J., Bertrand G., Najman L., Couprie M. Watershed cuts: minimum spanning forests and the drop of water principle. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31:1362-1374.
10. Fatakdawala H., Basavanhally A., Xu J., Bhanot G., Ganesan S., Feldman M., Tomaszewski J., Madabhushi A. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology. IEEE Transactions on Biomedical Engineering 2010, 57(7):1676-1689.
11. Felzenszwalb P.F., Huttenlocher D.P. Efficient graph-based image segmentation. International Journal of Computer Vision 2004, 59:167-181.
12. Galun, M., Sharon, E., Basri, R., Brandt, A., 2003. Texture segmentation by multiscale aggregation of filter responses and shape elements. In: Proceedings of the Ninth IEEE International Conference of Computer Vision (ICCV 2003), vol. 2, pp. 716-723.
13. Geman S., Geman D. Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. Readings in Uncertain Reasoning 1990, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 452-472.
14. Grady L. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28(11):1768-1783.
15. Grady, L., 2006. Fast, quality, segmentation of large volumes - isoperimetric distance trees. In: Proceedings of the 9th European Conference on Computer Vision (ECCV 2006), pp. 449-62.
16. Graf F., Grzegorzek M., Paulus D. Counting lymphocytes in histopathology images using connected component. Proceedings of the 20th International Conference of Pattern Recognition (ICPR) 2010 Contests 2010, Springer.
17. Hofmann, T., Puzicha, J., Buhmann, J.M., 1997. An optimization approach to unsupervised hierarchical texture segmentation. In: Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set, vol. 3, pp. 213-216.
18. Kuse M., Sharma T., Gupta S. A classification scheme for lymphocyte segmentation in H&E stained histology images. Proceedings of the 20th International Conference of Pattern Recognition (ICPR) 2010 Contests 2010, 235-243. Springer.
19. Malladi R., Sethian J.A. Level set methods for curvature flow, image enhancement, and shape recovery in medical images. Proceedings of the Conference on Visualization and Mathematics 1997, 329-345. Springer-Verlag.
20. Meyer F. Topographic distance and watershed lines. Signal Processing 1994, 38:113-125.
21. Meyer F. Morphological Segmentation Revisited. Space, Structure and Randomness 2005, Springer, Heidelberg, 315-347.
22. Mumford D., Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 1989, 42(5):577-684.
23. Nguyen H.T., Worring M., van den Boomgaard R. Watersnakes: energy-driven watershed segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25:330-342.
24. Panagiotakis C., Ramasso E., Tziritas G. Lymphocyte Segmentation using the Transferable Belief Model. Proceedings of the 20th International Conference of Pattern Recognition (ICPR) 2010 Contests 2010, Springer.
25. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22:888-905.
26. Staal J., Kalitzin S., Romeny B.T.H., Viergever M. Detection of critical structures in scale space. Scale-Space Theories in Computer Vision 1999, 105-116.
27. Tai, X.C., Hodneland, E., Weickert, J., Bukoreshtliev, N.V., Lundervold, A., Gerdes, H.H., 2007. Level set methods for watershed image segmentation. In: Proceedings of the of the 1st International Conference in Scale Space and Variational Methods in Computer Vision (SSVM 2007), pp. 178-90.
28. Tek, H.D., Comaniciu, D., Williams, J.P., 2001. Vessel detection by mean shift based ray propagation. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 228-235.
29. Vedaldi, A., Fulkerson, B., 2010. VLFeat - an open and portable library of computer vision algorithms. In: Proceedings of the 18th Annual ACM International Conference on Multimedia (MM 2010), pp. 1469-1472.
30. Xu C., Prince J.L. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 1998, 7(3):359-369.
31. Yu, S.X., 2004. Segmentation using multiscale cues. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), pp. 247-254.
32. Yu, S.X., 2005. Segmentation induced by scale invariance. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 444-451.
33. Yu, S.X., Shi, J., 2001. Understanding popout through repulsion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pp. 752-757.
34. Yu, S.X., Shi, J., 2003. Multiclass spectral clustering. In: Proceedings of the Ninth International Conference of Computer Vision (ICCV 2003), pp. 313-319.
35. Yu S.X., Shi J. Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26:173-183.
36. Zhu S.C., Yuille A. Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18:884-900.