Automatic joint classification and segmentation of whole cell 3D images

Pattern Recognition

Volumn 42, Issue 6, 2009, Pages 1067-1079

  Narasimha, Rajesh ^a,b   Ouyang, Hua ^b   Gray, Alexander ^b   McLaughlin, Steven W ^b   Subramaniam, Sriram ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Automated techniques; Cancer detection; Classification; Machine learning; Mitochondria; Segmentation; Texture features

Indexed keywords

ACOUSTIC INTENSITY; ADAPTIVE BOOSTING; CELL MEMBRANES; CLASSIFIERS; COMPUTATIONAL COMPLEXITY; CYTOLOGY; DIAGNOSTIC RADIOGRAPHY; ELECTRIC IMPEDANCE TOMOGRAPHY; ELECTRON MICROSCOPES; ELECTRON MICROSCOPY; ELECTRONS; LEARNING SYSTEMS; MEDICAL IMAGING; MITOCHONDRIA; ROBOT LEARNING; SCANNING ELECTRON MICROSCOPY; SIGNAL TO NOISE RATIO; SUPPORT VECTOR MACHINES; TEXTURES; TOMOGRAPHY; TWO DIMENSIONAL;

AUTOMATED TECHNIQUES; CANCER DETECTION; CLASSIFICATION; MACHINE LEARNING; SEGMENTATION; TEXTURE FEATURES;

THREE DIMENSIONAL;

EID: 59149098932     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.08.009     Document Type: Article

References (37)

1. Heymann J., Shi D., Kim S., Bliss D., Milne J., and Subramaniam S. 3D imaging of melanoma cells using automated ion abrasion scanning electron microscopy. Microscopy and Microanalysis 13 Suppl 2 (2007)
2. J.A. Heymann, D. Shi, S. Kim, D. Bliss, L.S. Jacqueline, S. Subramaniam, Nanometer resolution 3D imaging of mammalian cells with ion-abrasion scanning electron microscopy (2007), submitted for publication.
3. Heymann J.A., Hayles M., Gestmann I., Giannuzzi L.A., Lich B., and Subramaniam S. Site-specific 3D imaging of cells and tissues with a dual beam microscope. Journal of Structual Biology 155 (2006) 63-73
4. J. Winn, A. Criminisi, T. Minka, Object Categorization by Learned Universal Visual Dictionary, in: Proceedings of the 2005 IEEE International Conference on Computer Vision (ICCV), Beijing, 2005.
5. Varma M., and Zisserman A. A statistical approach to texture classification from single images. Int. J. Comp. Vision 62 (2005) 61-81
6. Leung T., and Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comp. Vision 43 (2001) 29-44
7. Cula O.G., and Dana K.J. Compact representation of bidirectional texture functions. Proc. Comput. Vision Pattern Recognition 1 (2001) 1041-1047
8. Zhang J., Marszalek M., Lazebnik S., and Schmid C. Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comp. Vision 73 (2007)
9. Kaufhold J., Collins R., Hoogs A., and Rondot P. Recognition and segmentation of scene content using region-based classification. Proceedings of the 18th International Conference on Pattern Recognition vol. 1 (2006) 755-760
10. Martin D.R., Fowlkes C.C., and Malik J. Learning to detect natural image boundaries using brightness color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2002) 1-20
11. Malik J., Belongie S., Leung T., and Shi J. Contour and texture analysis for image segmentation. Int. J. Comp. Vision 43 (2001) 7-27
12. Zhang H., Berg A.C., Maire M., and Malik J. SVM-KNN: discriminative nearest neighbor classification for visual category recognition. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2 (2006)
13. O. Tuzel, L. Yang, P. Meer, D.J. Foran, Classification of hematologic malignancies using texton signatures, Pattern Anal. Appl. 2006.
14. Issam E.N., Yang Y.Y., Miles N., Galatsanos P., and Nishikawa R.M. A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging 21 (2002) 1552-1563
15. Adjeroh D.A., Kandaswamy U., and Odom J.V. Texton-based segmentation of retinal vessels. J. Opt. Soc. Am. A 24 (2007) 1384-1393
16. Bossche K.V.D., Naeyaert J.M., and Lambert J. The quest for the mechanism of melanin transfer. Traffic 7 (2006) 769-778
17. Rosania G.R. Mitochondria give cells a tan. Chem. Bio. 12 (2005) 412-413
18. Gilboa G., Sochen N., and Zeevi Y.Y. Variational denoising of partly-textured images by spatially varying constraints. IEEE Trans. Image Process. 15 (2006) 2281-2289
19. Perona P., and Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990) 629-639
20. Rudin L., Osher S., and Fatemi E. Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60 (1992) 259-268
21. McIntosh J.R. Cellular Electron Microscopy (2007), Academic Press, New York
22. R. Narasimha, I. Aganj, A. Bennett, M. Borgnia, D. Zabransky, G. Sapiro, S.W. McLaughlin, J.L.S. Milne, S. Subramaniam, Evaluation of denoising algorithms for biological electron tomography, J. Struct. Bio. (2008), in press.
23. Osher S., and Paragios N. Geometric level set methods in imaging vision and graphics (2003), Springer, Berlin
24. Li C., Xu C., Gui C., and Fox M.D. Level set evolution without re-initialization: a new variational formulation. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) vol. 1 (2005)
25. Schmid C. Constructing models for content-based image retrieval. Proc. IEEE Conf. Comput. Vision Pattern Recognition 2 (2001)
26. J. Eichhorn, O. Chapelle, Object categorization with SVM: kernels for local features, Max Planck Institute for Biological Cybernetics, Technical Report No. 137, 2004.
27. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
28. Platt J.C. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B., Burges C., and Smola A. (Eds). Advances in Kernel Methods-Support Vector Learning (1999) 185-208
29. R.E. Schapire, A brief introduction to boosting, in: Proceedings of the 16th International Joint Conference on Artificial Intelligence, 1999.
30. 〈http://www.cartage.org.lb/en/themes/sciences/zoology/AnimalPhysiology/Anatomy/AnimalCellS-trucure/Mitochondria/Mitochondria.htm.
31. J.S. Modica-Napolitano, K.K. Singh, Mitochondria as targets for detection and treatment of cancer, Expert Rev. Mol. Med. (2006).
32. Schroeder A., Avnir Y., Weisman S., Najajreh Y., Gabizon A., Kost Y.T.J., and Barenholz Y. Controlling liposomal drug release with low frequency ultrasound: mechanism and feasibility. Langmuir 23 (2007) 4019-4025
33. Abraham S.A., Waterhouse D.N., Mayer L., Cullis P., Madden T., and Bally M.B. Liposomal formulations of doxorubicin. In: Duzgunes N. (Ed). Methods in Enzymology, Liposomes, Part E vol. 391 (2005), Elsevier Academic Press 71-97
34. Weickert J. Anisotropic Diffusion in Image Processing (1998), Teubner-Verlag, Stuttgart, Germany
35. Lengyel J.S., Milne J.L.S., and Subramaniam S. Electron tomography in nanoparticle imaging and analysis. Nanomedicine 3 (2008) 125-131
36. A. Moore, A. Genovese, A. Gray, L. Grone, N.K. II, R. Nichol, J. Schneider, A. Szalay, I. Szapudi, L. Wasserman, Fast algorithms and efficient statistics: N-point correlation functions, in: A.J. Banday, S. Zaroubi, M. Bartelmann (Eds.), Mining the Sky: Proceedings of the MPA/ESO/MPE workshop, Garching, 2001.
37. T.Liu, A.W. Moore, A. Gray, K. Yang, An investigation of practical approximate nearest neighbor algorithms, in: Proceedings of Neural Information Processing Systems (NIPS 2004), 2004.