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Linked References

1. V. Nandakumar, L. Kelbauskas, R. Johnson, and D. Meldrum, “Quantitative characterization of preneoplastic progression using single-cell computed tomography and three-dimensional karyometry,” Cytometry Part A, vol. 79, no. 1, pp. 25–34, 2011. View at Publisher · View at Google Scholar · View at Scopus
2. C. C. Reyes-Aldasoro, L. J. Williams, S. Akerman, C. Kanthou, and G. M. Tozer, “An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections,” Journal of Microscopy, vol. 242, no. 3, pp. 262–278, 2011. View at Publisher · View at Google Scholar · View at PubMed
3. J. Z. Cheng, Y. H. Chou, C. S. Huang et al., “ACCOMP: augmented cell competition algorithm for breast lesion demarcation in sonography,” Medical Physics, vol. 37, no. 12, pp. 6240–6252, 2010. View at Publisher · View at Google Scholar · View at Scopus
4. J. S. Schildkraut, N. Prosser, A. Savakis et al., “Level-set segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior,” Medical Physics, vol. 37, no. 11, pp. 5703–5710, 2010. View at Publisher · View at Google Scholar · View at Scopus
5. M. E. Plissiti, C. Nikou, and A. Charchanti, “Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images,” Pattern Recognition Letters, vol. 32, no. 6, pp. 838–853, 2011. View at Publisher · View at Google Scholar
6. M. E. Plissiti, C. Nikou, and A. Charchanti, “Automated detection of cell nuclei in Pap smear images using morphological reconstruction and clustering,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 233–241, 2011. View at Publisher · View at Google Scholar · View at PubMed
7. E. Díaz, G. Ayala, M. E. Díaz, L. W. Gong, and D. Toomre, “Automatic detection of large dense-core vesicles in secretory cells and statistical analysis of their intracellular distribution,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, Article ID 4468698, pp. 2–11, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
8. F. Brun, A. Accardo, M. Marchini, F. Ortolani, G. Turco, and S. Paoletti, “Texture analysis of TEM micrographs of alginate gels for cell microencapsulation,” Microscopy Research and Technique, vol. 74, no. 1, pp. 58–66, 2011. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
9. S. Amini, D. Veilleux, and I. Villemure, “Tissue and cellular morphological changes in growth plate explants under compression,” Journal of Biomechanics, vol. 43, no. 13, pp. 2582–2588, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
10. Y. Xiong, C. Kabacoff, J. Franca-Koh, P. N. Devreotes, D. N. Robinson, and P. A. Iglesias, “Automated characterization of cell shape changes during amoeboid motility by skeletonization,” BMC Systems Biology, vol. 4, article 33, 2010. View at Publisher · View at Google Scholar · View at PubMed
11. Y. A. Xiong and P. A. Iglesias, “Tools for analyzing cell shape changes during chemotaxis,” Integrative Biology, vol. 2, no. 11-12, pp. 561–567, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
12. Z. Q. Hong, L. M. Tao, and L. Li, “Effect of stress on mRNA expression of H+-ATPase in osteoclasts,” Molecular and Cellular Biochemistry, vol. 343, no. 1-2, pp. 183–190, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
13. Y. S. Lin, C. C. Lin, Y. S. Tsai, T. C. Ku, Y. H. Huang, and C. N. Hsu, “A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images,” Bioinformatics, vol. 26, no. 12, Article ID btq194, pp. i29–i37, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
14. P. Venkatesan, S. Das, M. M. R. Krishnan, C. Chakraborty, K. Chaudhury, and M. Mandal, “Effect of AEE788 and/or Celecoxib on colon cancer cell morphology using advanced microscopic techniques,” Micron, vol. 41, no. 3, pp. 247–256, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
15. S. Ohnuki, S. Nogami, and Y. Ohya, “A microfluidic device to acquire high-magnification microphotographs of yeast cells,” Cell Division, vol. 4, article 5, 2009. View at Publisher · View at Google Scholar · View at PubMed
16. P. W. Huang and Y. H. Lai, “Effective segmentation and classification for HCC biopsy images,” Pattern Recognition, vol. 43, no. 4, pp. 1550–1563, 2010. View at Publisher · View at Google Scholar · View at Scopus
17. F. H. Li, X. B. Zhou, J. W. Ma, and S. T. C. Wong, “Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis,” IEEE Transactions on Medical Imaging, vol. 29, no. 1, Article ID 5175475, pp. 96–105, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
18. Q. Chaudry, S. H. Raza, A. N. Young, and M. D. Wang, “Automated renal cell carcinoma subtype classification using morphological, textural and wavelets based features,” Journal of Signal Processing Systems, vol. 55, no. 1–3, pp. 15–23, 2009. View at Publisher · View at Google Scholar
19. M. Arbyn, M. Rebolj, I. M. C. M. De Kok et al., “The challenges of organising cervical screening programmes in the 15 old member states of the European Union,” European Journal of Cancer, vol. 45, no. 15, pp. 2671–2678, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
20. S. W. K. Chan, K. S. Leung, and W. S. F. Wong, “An expert system for the detection of cervical cancer cells using knowledge-based image analyzer,” Artificial Intelligence in Medicine, vol. 8, no. 1, pp. 67–90, 1996. View at Publisher · View at Google Scholar · View at Scopus
21. L. Ficsor, V. S. Varga, A. Tagscherer, Z. Tulassay, and B. Molnar, “Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis,” Cytometry Part A, vol. 73, no. 3, pp. 230–237, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
22. A. Tárnok, G. K. Valet, and F. Emmrich, “Systems biology and clinical cytomics: the 10th Leipziger Workshop and the 3rd International Workshop on Slide-Based Cytometry,” Cytometry Part A, vol. 69, no. 1, pp. 36–40, 2006. View at Publisher · View at Google Scholar · View at Scopus
23. G. López-Velázquez, J. Márquez, E. Ubaldo, G. Corkidi, O. Echeverría, and G. H. Vázquez Nin, “Three-dimensional analysis of the arrangement of compact chromatin in the nucleus of G₀ rat lymphocytes,” Histochemistry and Cell Biology, vol. 105, no. 2, pp. 153–161, 1996. View at Publisher · View at Google Scholar
24. D. Glotsos, P. Spyridonos, D. Cavouras, P. Ravazoula, P. A. Dadioti, and G. Nikiforidis, “An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine,” Medical Informatics and the Internet in Medicine, vol. 30, no. 3, pp. 179–193, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
25. C. O. De Solórzano, S. Costes, D. E. Callahan, B. Parvin, and M. H. Barcellos-Hoff, “Applications of quantitative digital image analysis to breast cancer research,” Microscopy Research and Technique, vol. 59, no. 2, pp. 119–127, 2002. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
26. G. A. Losa and C. Castelli, “Nuclear patterns of human breast cancer cells during apoptosis: characterisation by fractal dimension and co-occurrence matrix statistics,” Cell and Tissue Research, vol. 322, no. 2, pp. 257–267, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
27. T. Q. Xie, M. L. Zeidel, and Y. T. Pan, “Detection of tumorigenesis in urinary bladder with optical coherence tomography: optical characterization of morphological changes,” Optics Express, vol. 10, no. 24, pp. 1431–1443, 2002. View at Scopus
28. M. Colombi, L. Moro, N. Zoppi, and S. Barlati, “Quantitative evaluation of mRNAs by in situ hybridization and image analysis: principles and applications,” DNA and Cell Biology, vol. 12, no. 7, pp. 629–636, 1993. View at Scopus
29. W. Y. Xu-Van Opstal, C. Ranger, O. Lejeune et al., “Automated image analyzing system for the quantitative study of living cells in culture,” Microscopy Research and Technique, vol. 28, no. 5, pp. 440–447, 1994. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
30. P. A. Melrose, C. Pickel, H. S. Cheramie, W. G. Henk, M. A. Littlefield-Chabaud, and D. D. French, “Distribution and morphology of immunoreactive gonadotropin-releasing hormone (GnRH) neurons in the basal forebrain of ponies,” Journal of Comparative Neurology, vol. 339, no. 2, pp. 269–287, 1994. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
31. M. F. Villa and F. R. Amthor, “Automating the quantitative analysis of 2-D neural dendritic trees,” Journal of Neuroscience Methods, vol. 56, no. 1, pp. 77–88, 1995. View at Publisher · View at Google Scholar · View at Scopus
32. M. Masseroli, A. Bollea, and G. Forloni, “Quantitative morphology and shape classification of neurons by computerized image analysis,” Computer Methods and Programs in Biomedicine, vol. 41, no. 2, pp. 89–99, 1993. View at Publisher · View at Google Scholar · View at Scopus
33. M. C. Albertini, A. Accorsi, L. Teodori et al., “Use of multiparameter analysis for Vibrio alginolyticus viable but nonculturable state determination,” Cytometry Part A, vol. 69, no. 4, pp. 260–265, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
34. E. Vlodavsky, E. Palzur, and J. F. Soustiel, “Hyperbaric oxygen therapy reduces neuroinflammation and expression of matrix metalloproteinase-9 in the rat model of traumatic brain injury,” Neuropathology and Applied Neurobiology, vol. 32, no. 1, pp. 40–50, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
35. K. A. Giuliano, “Dissecting the individuality of cancer cells: the morphological and molecular dynamics of single human glioma cells,” Cell Motility and the Cytoskeleton, vol. 35, no. 3, pp. 237–253, 1996. View at Publisher · View at Google Scholar · View at Scopus
36. B. Rousset, Y. Munari-Silem, V. Gire, and P. Fonlupt, “Dynamic analysis of drug action on in vitro reconstituted thyroid follicle by microinjection of tracer molecules and videomicroscopy,” Cell Biology and Toxicology, vol. 8, no. 3, pp. 1–7, 1992. View at Publisher · View at Google Scholar · View at Scopus
37. C. Broglio, J. Dufer, P. Joly, Y. Carpentier, and A. Desplaces, “Quantitative morphological assessment of erythroblastic differentiation induced, in vitro, in human K562 leukemic cells,” Analytical Cellular Pathology, vol. 5, no. 3, pp. 135–146, 1993. View at Scopus
38. L. Vega-Alvarado, J. Márquez, and G. Corkidi, “Inter-chromosome texture as a feature for automatic identification of metaphase spreads,” Medical and Biological Engineering and Computing, vol. 40, no. 4, pp. 479–484, 2002. View at Scopus
39. N. Kutsuna and S. Hasezawa, “Morphometrical study of plant vacuolar dynamics in single cells using three-dimensional reconstruction from optical sections,” Microscopy Research and Technique, vol. 68, no. 5, pp. 296–306, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
40. D. A. Winkelmann, T. S. Baker, and I. Rayment, “Three-dimensional structure of myosin subfragment-1 from electron microscopy of sectioned crystals,” Journal of Cell Biology, vol. 114, no. 4, pp. 701–713, 1991. View at Scopus
41. T. G. Li, S. P. Wang, and N. Zhao, “Gray-scale edge detection for gastric tumor pathologic cell images by morphological analysis,” Computers in Biology and Medicine, vol. 39, no. 11, pp. 947–952, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
42. J. Poikonen and A. Paasio, “An 8 × 8 cell analog order-statistic-filter array with asynchronous grayscale morphology in 0.13-μm CMOS,” IEEE Transactions on Circuits and Systems I, vol. 56, no. 8, pp. 1541–1553, 2009. View at Publisher · View at Google Scholar · View at Scopus
43. J. Angulo and S. Matou, “Application of mathematical morphology to the quantification of in vitro endothelial cell organization into tubular-like structures,” Cellular and Molecular Biology, vol. 53, no. 2, pp. 22–35, 2007. View at Publisher · View at Google Scholar · View at Scopus
44. D. C. Walker, B. H. Brown, A. D. Blacket, J. Tidy, and R. H. Smallwood, “A study of the morphological parameters of cervical squamous epithelium,” Physiological Measurement, vol. 24, no. 1, pp. 121–135, 2003. View at Publisher · View at Google Scholar · View at Scopus
45. F. Ortiz, F. Torres, E. De Juan, and N. Cuenca, “Colour mathematical morphology for neural image analysis,” Real-Time Imaging, vol. 8, no. 6, pp. 455–465, 2002. View at Scopus
46. S. Li, K. H. Hu, N. Cai et al., “Automatic analysis of image of surface structure of cell wall-deficient EVC,” Bio-Medical Materials and Engineering, vol. 11, no. 3, pp. 159–166, 2001. View at Scopus
47. V. Metzler, T. Lehmann, H. Bienert, K. Mottaghy, and K. Spitzer, “Scale-independent shape analysis for quantitative cytology using mathematical morphology,” Computers in Biology and Medicine, vol. 30, no. 3, pp. 135–151, 2000. View at Publisher · View at Google Scholar · View at Scopus
48. G. Diaz, A. Zucca, M. D. Setzu, and C. Cappai, “Chromatin pattern by variogram analysis,” Microscopy Research and Technique, vol. 39, no. 3, pp. 305–311, 1997. View at Scopus
49. S. Holmes, A. Kapelner, and P. P. Lee, “An interactive java statistical image segmentation system: gemIdent,” Journal of Statistical Software, vol. 30, no. 10, pp. 1–20, 2009.
50. J. B. Hendricks, “Quantitative histology by laser scanning cytometry,” Journal of Histotechnology, vol. 24, no. 1, pp. 59–62, 2001. View at Scopus
51. E. Ficarra, S. Di Cataldo, A. Acquaviva, and E. Macii, “Automated segmentation of cells with IHC membrane staining,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 5, pp. 1421–1429, 2011. View at Publisher · View at Google Scholar · View at PubMed
52. K. S. Cheng, C. J. Chien, M. H. Hsu, and C. L. Li, “Development of a PC12 cell cultivation and monitoring system for neuronal-like study,” Biomedical Engineering, vol. 22, no. 3, pp. 193–203, 2010. View at Publisher · View at Google Scholar · View at Scopus
53. O. Schmitt and M. Hasse, “Morphological multiscale decomposition of connected regions with emphasis on cell clusters,” Computer Vision and Image Understanding, vol. 113, no. 2, pp. 188–201, 2009. View at Publisher · View at Google Scholar · View at Scopus
54. O. Schmitt and M. Hasse, “Radial symmetries based decomposition of cell clusters in binary and gray level images,” Pattern Recognition, vol. 41, no. 6, pp. 1905–1923, 2008. View at Publisher · View at Google Scholar · View at Scopus
55. C. López, M. Lejeune, M. T. Salvadó et al., “Automated quantification of nuclear immunohistochemical markers with different complexity,” Histochemistry and Cell Biology, vol. 129, no. 3, pp. 379–387, 2008. View at Publisher · View at Google Scholar · View at PubMed
56. P. Thurner, R. Müller, G. Raeber, U. Sennhauser, and J. A. Hubbell, “3D morphology of cell cultures: a quantitative approach using micrometer synchrotron light tomography,” Microscopy Research and Technique, vol. 66, no. 6, pp. 289–298, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
57. G. Landini and I. E. Othman, “Architectural analysis of oral cancer, dysplastic, and normal epithelia,” Cytometry Part A, vol. 61, no. 1, pp. 45–55, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
58. R. Nakajima, T. Nakamura, M. Ogawa, H. Miyakawa, and Y. Kudo, “Novel method for quantification of brain cell swelling in rat hippocampal slices,” Journal of Neuroscience Research, vol. 76, no. 5, pp. 723–733, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
59. M. Kruk, S. Osowski, and R. Koktysz, “Recognition and classification of colon cells applying the ensemble of classifiers,” Computers in Biology and Medicine, vol. 39, no. 2, pp. 156–165, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
60. F. Y. Shih, C. T. King, and C. C. Pu, “Pipeline architectures for recursive morphological operations,” IEEE Transactions on Image Processing, vol. 4, no. 1, pp. 11–18, 1995. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
61. J. L. Humm, R. M. Macklis, Y. Yang, K. Bump, and L. M. Chin, “Image analysis for the study of radionuclide distribution in tissue sections,” Journal of Nuclear Medicine, vol. 35, no. 7, pp. 1217–1225, 1994. View at Scopus
62. S. Jiang, X. Zhou, T. Kirchhausen, and S. T. C. Wong, “Detection of molecular particles in live cells via machine learning,” Cytometry Part A, vol. 71, no. 8, pp. 563–575, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
63. K. Jiang, Q. M. Liao, and Y. Xiong, “A novel white blood cell segmentation scheme based on feature space clustering,” Soft Computing, vol. 10, no. 1, pp. 12–19, 2006. View at Publisher · View at Google Scholar · View at Scopus
64. H. S. Wu, R. Xu, N. Harpaz, D. Burstein, and J. Gil, “Segmentation of intestinal gland images with iterative region growing,” Journal of Microscopy, vol. 220, no. 3, pp. 190–204, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
65. G. Haroske, V. Dimmer, K. Friedrich et al., “Nuclear image analysis of immunohistochemically stained cells in breast carcinomas,” Histochemistry and Cell Biology, vol. 105, no. 6, pp. 479–485, 1996. View at Publisher · View at Google Scholar · View at Scopus
66. J. M. Higgins, D. T. Eddington, S. N. Bhatia, and L. Mahadevan, “Statistical dynamics of flowing red blood cells by morphological image processing,” PLoS Computational Biology, vol. 5, no. 2, Article ID e1000288, 2009. View at Publisher · View at Google Scholar · View at PubMed
67. M. Garcia-Bonafe and A. Moragas, “Differential diagnosis of malignant and reactive cells from serous effusions: image and texture analysis study,” Analytical Cellular Pathology, vol. 12, no. 2, pp. 85–98, 1996. View at Scopus
68. C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 430–436, June 2005.
69. A. Tsai, A. Yezzi, and A. S. Willsky, “Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification,” IEEE Transactions on Image Processing, vol. 10, no. 8, pp. 1169–1186, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Zentralblatt MATH · View at Scopus
70. T. Chan and L. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Zentralblatt MATH · View at Scopus
71. Asa Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, “Support vector machine clustering,” Journal of Machine Learning Research, vol. 2, pp. 125–137, 2001.
72. H. Xue, S. Chen, and Q. Yang, “Structural support vector machine,” Lecture Notes in Computer Science, vol. 5263, no. 1, pp. 501–511, 2008. View at Publisher · View at Google Scholar · View at Scopus
73. G. Zellnig, A. Perktold, and B. Zechmann, “Fine structural quantification of drought-stressed Picea abies (L.) organelles based on 3D reconstructions,” Protoplasma, vol. 243, no. 1–4, pp. 129–136, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
74. J. M. Messerli, M. E. Eppenberger-Eberhardt, B. M. Rutishauser et al., “Remodelling of cardiomyocyte cytoarchitecture visualized by three-dimensional (3D) confocal microscopy,” Histochemistry, vol. 100, no. 3, pp. 193–202, 1993. View at Scopus
75. H. A. McNally and R. B. Borgens, “Three-dimensional imaging of living and dying neurons with atomic force microscopy,” Journal of Neurocytology, vol. 33, no. 2, pp. 251–258, 2004. View at Publisher · View at Google Scholar · View at Scopus
76. D. Comaniciu, P. Meer, and D. J. Foran, “Image-guided decision support system for pathology,” Machine Vision and Applications, vol. 11, no. 4, pp. 213–224, 1998. View at Scopus
77. D. Ristanović, N. T. Milošević, I. B. Stefanović, D. Marić, and I. Popov, “Cell image area as a tool for neuronal classification,” Journal of Neuroscience Methods, vol. 182, no. 2, pp. 272–278, 2009. View at Publisher · View at Google Scholar · View at PubMed