- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 176519, 11 pages
Brain Tumor Classification Using AFM in Combination with Data Mining Techniques
1School of Applied Health and Social Sciences, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria
2Department of Pathology, Nerve Clinic Linz Wagner Jauregg, Wagner-Jauregg-Weg 15, 4020 Linz, Austria
3University of Applied Sciences Upper Austria, Research & Development Wels, Stelzhamerstraße 23, 4600 Wels, Austria
Received 30 April 2013; Accepted 18 July 2013
Academic Editor: Kaisorn L. Chaichana
Copyright © 2013 Marlene Huml 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.
- D. N. Louis, H. Ohgaki, O. D. Wiestler et al., Eds., The 2007 WHO Classification of Tumors of the Central Nervous System, IARC Press, Lyon, France, 2007.
- J. Felsberg and G. Reifenberger, “Neuropathology and molecular genetics of diffusely infiltrating cerebral gliomas,” Medical Laser Application, vol. 17, no. 2, pp. 133–146, 2002.
- M. J. Riemenschneider and G. Reifenberger, “Astrocytic tumors,” Recent Results in Cancer Research, vol. 171, pp. 3–24, 2009.
- K. K. Herfarth, S. Gutwein, and J. Debus, “Postoperative radiotherapy of astrocytomas,” Seminars in Surgical Oncology, vol. 20, no. 1, pp. 13–23, 2001.
- L. M. DeAngelis, “Brain tumors,” The New England Journal of Medicine, vol. 344, no. 2, pp. 114–123, 2001.
- W. Coons, P. Johnson, B. Scheithauer, A. Yates, and D. Pearl, “Improving diagnostic accuracy and interobserver concordance in the classification and grading of Primary Gliomas,” Cancer, vol. 79, pp. 1381–1393, 1997.
- A. Bonfiglio, M. T. Parodi, and G. P. Tonini, “Subcellular details of early events of differentiation induced by retinoic acid in human neuroblastoma cells detected by atomic force microscope,” Experimental Cell Research, vol. 216, no. 1, pp. 73–79, 1995.
- S. Nagayama, M. Morimoto, K. Kawanata et al., “AFM observation of three-dimensional fine structural changes in living neurons,” Bioimages, vol. 4, pp. 111–116, 1996.
- S. Nagayama, T. Tojima, M. Morimoto et al., “Practical scan speed in atomic force microscopy for live neurons in a physiological solution,” Japanese Journal of Applied Physics, vol. 36, no. 6, pp. 3877–3880, 1997.
- V. Parpura, P. G. Haydon, and E. Henderson, “Three-dimensional imaging of living neurons and glia with the automic force microscope,” Journal of Cell Science, vol. 104, no. 2, pp. 427–432, 1993.
- E. Henderson, P. G. Haydon, and D. S. Sakaguchi, “Actin filament dynamics in living glial cells imaged by atomic force microscopy,” Science, vol. 257, no. 5078, pp. 1944–1946, 1992.
- E. Henderson and D. S. Sakaguchi, “Imaging F-actin in fixed glial cells with a combined optical fluorescence/atomic force microscope,” NeuroImage, vol. 1, no. 2, pp. 145–150, 1993.
- L. Teodori, M. C. Albertini, F. Uguccioni et al., “Static magnetic fields affect cell size, shape, orientation, and membrane surface of human glioblastoma cells, as demonstrated by electron, optic, and atomic force microscopy,” Cytometry A, vol. 69, no. 2, pp. 75–85, 2006.
- E. Bystrenova, M. Jelitai, I. Tonazzini et al., “Neural networks grown on organic semiconductors,” Advanced Functional Materials, vol. 18, no. 12, pp. 1751–1756, 2008.
- Y. Yamane, H. Shiga, H. Asou et al., “Dynamics of astrocyte adhesion as analyzed by a combination of atomic force microscopy and immunocytochemistry: the involvement of actin filaments and connexin 43 in the early stage of adhesion,” Archives of Histology and Cytology, vol. 62, no. 4, pp. 355–361, 1999.
- H. L. Fillmore, I. Chasiotis, S. W. Cho, and G. T. Gillies, “Atomic force microscopy observations of tumour cell invadopodia: novel cellular nanomorphologies on collagen substrates,” Nanotechnology, vol. 14, no. 1, pp. 73–76, 2003.
- M. Melling, S. Hochmeister, R. Blumer et al., “Atomic force microscopy imaging of the human trigeminal ganglion,” NeuroImage, vol. 14, no. 6, pp. 1348–1352, 2001.
- M. Melling, D. Karimian-Teherani, M. Behnam, and S. Mostler, “Morphological study of the healthy human oculomotor nerve by atomic force microscopy,” NeuroImage, vol. 20, no. 2, pp. 795–801, 2003.
- K. Schilcher, R. Silye, and H. P. Huber, “Neuropil fiber reduction in brain tumors observed by atomic force microscopy,” Journal of Advanced Microscopy Research, vol. 5, pp. 86–90, 2010.
- A. Hutter, K. E. Schwetye, A. J. Bierhals, and R. C. McKinstry, “Brain neoplasms: epidemiology, diagnosis, and prospects for cost-effective imaging,” Neuroimaging Clinics of North America, vol. 13, no. 2, pp. 237–250, 2003.
- E. I. Papageorgiou, P. P. Spyridonos, D. T. Glotsos et al., “Brain tumor characterization using the soft computing technique of fuzzy cognitive maps,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 820–828, 2008.
- M. Law, S. Yang, H. Wang et al., “Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging,” American Journal of Neuroradiology, vol. 24, no. 10, pp. 1989–1998, 2003.
- I. S. Khayal, T. R. McKnight, C. McGue et al., “Apparent diffusion coefficient and fractional anisotropy of newly diagnosed grade II gliomas,” NMR in Biomedicine, vol. 22, no. 4, pp. 449–455, 2009.
- S. Cuellar-Baena, L. M. T. S. Morais, F. Cendes, A. V. Faria, and G. Castellano, “Manual and semi-automatic quantification of in vivo 1H-MRS data for the classification of human primary brain tumors,” Brazilian Journal of Medical and Biological Research, vol. 44, no. 4, pp. 345–353, 2011.
- D. J. Hemanth, C. K. Selva Vijila, and J. Anitha, “Application of neuro-fuzzy model for mr brain tumor image classification,” Biomedical Soft Computing and Human Sciences, vol. 16, no. 1, pp. 95–102, 1995.
- M. J. Mckeown and D. A. Ramsay, “Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net,” Journal of Neuropathology and Experimental Neurology, vol. 55, no. 12, pp. 1238–1245, 1996.
- C. Decaestecker, I. Salmon, O. Dewitte et al., “Nearest-neighbor classification for identification of aggressive versus nonaggressive low-grade astrocytic tumors by means of image cytometry- generated variables,” Journal of Neurosurgery, vol. 86, no. 3, pp. 532–537, 1997.
- P. K. Sallinen, S.-L. Sallinen, P. T. Helén et al., “Grading of diffusely infiltrating astrocytomas by quantitative histopathology, cell proliferation and image cytometric DNA analysis,” Neuropathology and Applied Neurobiology, vol. 26, no. 4, pp. 319–331, 2000.
- R. Nafe and W. Schlote, “Topometric analysis of diffuse astrocytomas,” Analytical and Quantitative Cytology and Histology, vol. 25, no. 1, pp. 12–18, 2003.
- R. Nafe and W. Schlote, “Densitometric analysis of tumor cell nuclei in low-grade and high-grade astrocytomas,” Electronic Journal of Pathology and Histology, vol. 8, no. 2-3, pp. 23021–230218, 2002.
- D. Glotsos, P. Spyridonos, P. Petalas et al., “Computer based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedure,” Analytical and Quantitative Cytology and Histology, vol. 26, no. 2, pp. 77–83, 2004.
- D. Glotsos, J. Tohka, P. Ravazoula, D. Cavouras, and G. Nikiforidis, “Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines,” International Journal of Neural Systems, vol. 15, no. 1-2, pp. 1–11, 2005.
- D. Glotsos, I. Kalatzis, P. Spyridonos et al., “Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme,” Computer Methods and Programs in Biomedicine, vol. 90, no. 3, pp. 251–261, 2008.
- D. C. Shubhangi and P. S. Hiremath, “Support Vector Machine (SVM) classifier for brain tumor detection,” in Proceedings of the International Conference on Advances in Computing, Communication and Control (ICAC3 '09), pp. 444–448, India, January 2009.
- C. M. Bentaouza and M. Benyettou, “Support vector machines for brain tumours cells classification,” Journal of Applied Sciences, vol. 10, no. 16, pp. 1755–1761, 2010.
- M. F. B. Othman, N. B. Abdullah, and N. F. B. Kamal, “MRI brain classification using support vector machine,” in Proceedings of the 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO '11), April 2011.
- C. L. Nutt, R. A. Betensky, M. A. Brower, T. T. Batchelor, D. N. Louis, and A. O. Stemmer-Rachamimov, “YKL-40 is a differential diagnostic marker for histologic subtypes of high-grade gliomas,” Clinical Cancer Research, vol. 11, no. 6, pp. 2258–2264, 2005.
- C. L. Nutt, D. R. Mani, R. A. Betensky et al., “Gene expression-based classification of malignant gliomas correlates better with survival than histological classification,” Cancer Research, vol. 63, no. 7, pp. 1602–1607, 2003.
- L. P. Petalidis, A. Oulas, M. Backlund et al., “Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data,” Molecular Cancer Therapeutics, vol. 7, no. 5, pp. 1013–1024, 2008.
- J. Schmalzing, M. Kerscher, and T. Buchert, “Minkowski functionals in cosmology,” in Proceedings of the International School of Physics Enrico Fermi. Course CII: Dark Matter in the Universe, S. Bonometto, J. Primack, and A. Provenzale, Eds., pp. 281–291, 1996.
- J. Schmalzing and T. Buchert, “Beyond genus statistics: a unifying approach to the morphology of cosmic structure,” Astrophysical Journal Letters, vol. 482, no. 1, pp. L1–L4, 1997.
- C. Räth, R. Monetti, J. Bauer et al., “Strength through structure: visualization and local assessment of the trabecular bone structure,” New Journal of Physics, vol. 10, Article ID 125010, 17 pages, 2008.
- H. F. Boehm, T. Schneider, S. M. Buhmann-Kirchhoff et al., “Automated classification of breast parenchymal density: topologic analysis of X-ray attenuation patterns depicted with digital mammography,” American Journal of Roentgenology, vol. 191, no. 6, pp. W275–W282, 2008.
- C. Decaestecker, I. Camby, N. Nagy, J. Brotchi, R. Kiss, and I. Salmon, “Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques,” Brain Pathology, vol. 8, no. 1, pp. 29–38, 1998.
- H. Martin, K. Voss, P. Hufnagl, and K. Frolich, “Automated image analysis of gliomas. An objective and reproducible method for tumor grading,” Acta Neuropathologica, vol. 63, no. 2, pp. 160–169, 1984.
- M. Scarpelli, R. Montironi, D. Thompson, and P. H. Bartels, “Computer-assisted discrimination of glioblastomas,” Analytical and Quantitative Cytology and Histology, vol. 19, no. 5, pp. 369–375, 1997.
- D. Schiffer, A. Chio, M. T. Giordana et al., “Histologic prognostic factors in ependymoma,” Child's Nervous System, vol. 7, no. 4, pp. 177–182, 1991.
- H. Kolles, A. Von Wangenheim, J. Rahmel, I. Niedermayer, and W. Feiden, “Data-driven approaches to decision making in automated tumor grading: an example of astrocytoma grading,” Analytical and Quantitative Cytology and Histology, vol. 18, no. 4, pp. 298–304, 1996.
- H. Martin and K. Voss, “Automated image analysis of glioblastomas and other gliomas,” Acta Neuropathologica, vol. 58, no. 1, pp. 9–16, 1982.
- M. Scarpelli, P. H. Bartels, R. Montironi, C. M. Galluzzi, and D. Thompson, “Morphometrically assisted grading of astrocytomas,” Analytical and Quantitative Cytology and Histology, vol. 16, no. 5, pp. 351–356, 1994.
- Hutterer St, G. Zauner, M. Huml, R. Silye, and K. Schilcher, “Data mining techniques for AFM- based tumor classification,” in Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '12), pp. 105–111, 2012.
- M. Affenzeller, S. M. Winkler, S. Wagner, and A. Beham, Genetic Algorithms and Genetic Programming, CRC Press, New York, NY, USA, 2009.
- J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, Mass, USA, 1992.