- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 101536, 10 pages
Recent Advances in Morphological Cell Image Analysis
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
3Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
4DreamSciTech Consulting, Shenzhen 518054, China
5Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
Received 29 August 2011; Accepted 3 October 2011
Academic Editor: Carlo Cattani
Copyright © 2012 Shengyong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- J. B. Hendricks, “Quantitative histology by laser scanning cytometry,” Journal of Histotechnology, vol. 24, no. 1, pp. 59–62, 2001.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- T. Chan and L. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001.
- 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.
- H. Xue, S. Chen, and Q. Yang, “Structural support vector machine,” Lecture Notes in Computer Science, vol. 5263, no. 1, pp. 501–511, 2008.
- 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.
- 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.
- 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.
- 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.
- 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.