Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images

IEEE Transactions on Information Technology in Biomedicine

Volumn 12, Issue 1, 2008, Pages 42-54

  Maglogiannis, Ilias ^a   Sarimveis, Haralambos ^b   Kiranoudis, C T ^b   Chatziioannou, Aristotelis A ^c   Oikonomou, Nikos ^d   Aidinis, Vassilis ^d  

^a UNIVERSITY OF THE AEGEAN   (Greece)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^c INSTITUTE OF BIOLOGY   (Greece)

^d Institute of Immunology   (Greece)

Author keywords

Image analysis; Interstitial pulmonary fibrosis; Neural networks; Quantitative assessment of microscopic images; Quantitative phenotypic classification; Support vector machines

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE RECOGNITION; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

IDIOPATHIC PULMONARY FIBROSIS; INTERSTITIAL PULMONARY FIBROSIS; MICROSCOPIC IMAGES; QUANTITATIVE PHENOTYPIC CLASSIFICATION;

MEDICAL IMAGING;

ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; FEMALE; LUNG FIBROSIS; MALE; METHODOLOGY; MICROSCOPY; MOUSE;

ANIMALS; FEMALE; MALE; MICE; MICROSCOPY; NEURAL NETWORKS (COMPUTER); PULMONARY FIBROSIS;

EID: 39449139992     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2006.888702     Document Type: Article

References (48)

1. M. Chalfie, Y. Tu, G. Euskirchen, W. W. Ward, and D. C. Prasher, "Green fluorescent protein as a marker for gene expression," Science, vol. 263, pp. 802-805, 1994.
2. S. Inoue and K. R. Spring, Video Microscopy. New York: Plenum, 1997.
3. D. M. Shotton, "Image resolution and digital image processing in electronic light microscopy," in Cell Biology, A Laboratory Handbook, vol. 3, J. E. Cells, Ed., 2nd ed. San Diego, CA: Academic, 1998, pp. 85-98.
4. L. Bocchi, G. Coppini, J. Nori, and G. Valli, "Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks," Med. Eng. Phys., vol. 26, no. 4, pp. 303-312, 2004.
5. J. Segyeong, S. Y. Yoon, K. M. Woo, and C. K. Hee, "Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features," IEEE Trans. Med. Imag., vol. 23, no. 10, pp. 1292-1300, Oct. 2004.
6. N. Theera-Umpon and P. D. Gader, "System-level training of neural networks for counting white blood cells," IEEE Trans. Syst., Man Cybern. C, vol. 32, no. 1, pp. 48-53, Feb. 2002.
7. G. Coppini, S. Diciotti, M. Falchim, N. Villari, and G. Valli, "Neural networks for computer-aided diagnosis: Detection of lung nodules in chest radiograms," IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 4, pp. 344-357, Dec. 2003.
8. C. Christodoulou, C. Pattichis, M. Pantziaris, and A. Nicolaides, "Texture-based classification of atherosclerotic carotid plaques," IEEE Trans. Med. Imag., vol. 22, no. 7, pp. 902-912, Jul. 2003.
9. J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units," Neural Comput., vol. 1, pp. 281-294, 1989.
10. C. Darken and J. Moody, "Fast adaptive k-means clustering: Some empirical results," in Proc. IEEE INNS Int. Joint Conf. Neural Netw. 1990, pp. 233-238.
11. J. C. Dunn, "A fuzzy relative to the ISODATA process and its use in detecting compact, well-separated clusters," J. Cybernet., vol. 3, pp. 32-57, 1973.
12. K. M. Antoniou, A. Pataka, D. Bouros, and N. M. Siafakas, "Pathogenetic pathways and novel pharmacotherapeutic targets in idiopathic pulmonary fibrosis," Pulmonary Pharmacol. Therapeutics, vol. 19, no. 2, pp. 139-147, 2006.
13. G. Izbiki, M. J. Segel, T. G. Christensen, M. W. Conner, and R. Breuer, "Time course of bleomycin-induced lung fibrosis," Int. J. Exp. Pathol., vol. 83, pp. 111-119, 2002.
14. G. Camps-Valls and L. L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
15. G. M. Foody and A. Mathur, "Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification," Remote Sens. Environ., vol. 93, pp. 107-117, 2004.
16. G. M. Foody and A. Mathur, "A relative evaluation of multiclass image classification by support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, pp. 1335-1343, Jun. 2004.
17. K. I. Kim, J. H. Kim, and K. Jung, "Face recognition using support vector machines with local correlation kernels," Int. J. Pattern Recognit. Artif. Intell., vol. 16, pp. 97-111, 2002.
18. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
19. V. N. Vapnik, Statistical Learning Theory. New York: Springer, 1998.
20. B. Schölkopf and A. J. Smola, Learning With Kernels - Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT Press, 2002.
21. P. J. Shaw, "Computational deblurring of fluorescence microscope images," in Cell Biology, A Laboratory Handbook, vol. 3, J. E. Cells, Ed., 2nd ed. San Diego, CA: Academic, 1998, pp. 206-217.
22. S. G. Megason and S. E. Fraser, "Digitizing life at the level of the cell: High-performance laser-scanning microscopy and image analysis for in toto imaging of development," Mech. Dev., vol. 120, pp. 1407-1420, 2003.
23. M. Barr, S. McClellan, T. Winokur, and G. Vaughn, "An automated tissue preclassification approach for telepathology: Implementation and performance analysis," IEEE Trans. Inf. Technol. Biomed., vol. 8, no. 2, pp. 97-102, Jun. 2004.
24. S. Cross, T. Dennis, and R. Start, "Telepathology: Current status and future prospects in diagnostic histopathology," Histopathology, vol. 41, pp. 91-109, 2002.
25. R. Weinstein, M. Descour, C. Liang, A. Bhattacharyya, A. Graham, J. Davis, K. Scott, L. Richter, E. Krupinski, J. Szymus, K. Kayser, and B. Dunn, "Telepathology overview: From concept to implementation," Hum. Pathol., vol. 32, no. 12, pp. 1283-1299, 2001.
26. S. McClellan, T. Winokur, and E. Kujawski, "A prototype system for telepathology," presented at the 1st Annu. Telecommun. Conf., Austin, TX, Oct. 1996.
27. W. Wolberg, W. Street, and O. Mangasarian, "Breast cytology diagnosis with digital image analysis," Anal. Quant. Cytol. Histol., vol. 15, no. 6, pp. 396-404, 1993.
28. T. Caballero, A. Pérez-Milena, M. Masseroli, F. O'Valle, F. J. Salmerón, R. M. G. Del Moral, and G. Sánchez-Salgado, "Liver fibrosis assessment with semiquantitative indexes and image analysis quantification in sustained-responder and non-responder," J. Hepatol., vol. 34, pp. 740-747, 2001.
29. M. Masseroli, T. Caballero, F. O'Valle, R. M. G. Del Moral, A. Pérrez-Milena, and R. G. Del Moral, "Automatic quantification of liver fibrosis: Design and validation of a new image analysis method. Comparison with semi-quantitative indexes of fibrosis," J. Hepatol. vol. 32, pp. 453-464, 2000.
30. M. Yagura, S. Murai, H. Kojima, H. Tokita, H. Kamitsukasa, and H. Harada, "Changes of liver fibrosis in chronic hepatitis C patients with no response to interferon-α therapy: Including quantitative assessment by a morphometric method," J. Gastroenterol., vol. 35, pp. 105-111, 2000.
31. P. Bedossa, D. Dargere, and V. Paradis, "Sampling variability of liver fibrosis in chronic hepatitis C," Hepatology, vol. 38, no. 6, pp. 1449-1457, 2003.
32. M. A. Hussain, S. N. Merchant, L. S. Mombasawala, and R. R. Pumiyani, "A decrease in effective diameter of rat mesenteric venules due to leukocyte margination after a bolus injection of pentoxifylline - Digital image analysis of an intravital microscopic observation," Microvasc. Res., vol. 67, pp. 237-244, 2004.
33. K. Shiraishi, H. Takihara, and K. Naito, "Quantitative analysis of testicular interstitial fibrosis after vasectomy in humans," Aktuelle Urol., vol. 34, no. 4, pp. 262-264, 2003.
34. K. Shiraishi, H. Takihara, and K. Naito, "Influence of interstitial fibrosis on spermatogenesis after vasectomy and vasovasostomy," Contraception, vol. 65, no. 3, pp. 245 249, 2002.
35. P. König, C. Kayser, V. Bonin, and R. P. Wurtz, "Efficient evaluation of serial sections by iterative Gabor matching," J. Neurosci. Methods, vol. 111, pp. 141-150, 2001.
36. G. Tsekouras, H. Sarimveis, and G. Bafas, "A method for fuzzy system identification based on clustering analysis," SAMS, vol. 42, no. 6, pp. 797-823, 2002.
37. G. Tsekouras, H. Sarimveis, C. Raptis, and G. Bafas, "A fuzzy logic approach for the classification of product qualitative characteristics," Comput. Chem. Eng., vol. 26, pp. 429-438, 2002.
38. M. Selman and A. Pardo. (2002) Idiopathic pulmonary fibrosis: An epithelial/fibroblast cross-talk disorder. Respir. Res. [Online]. 3, Available: http://respiratory-research.com/content/3/1/3
39. D. B. Coultas, R. E. Zumwalt, W. C. Black, and R. E. Sobonya, "The epidemiology of interstitial lung diseases," Am. J. Respir. Crit. Care Med., vol. 150, no. 4, pp. 967-972, 1994.
40. T. Gross and G. Hunninghake, "Idiopathic pulmonary fibrosis," N. Engl. J. Med., vol. 345, no. 7, pp. 517 525, 2001.
41. A. Pardo et al., "Idiopathic pulmonary fibrosis: Prevailing and evolving hypotheses about its pathogenesis and implications for therapy," Ann. Intern. Med., vol. 1334, pp. 136-151, 2001.
42. L. Ortiz et al., "Upregulation of the p75 but not the p55 TNF-α receptor mRNA after silica and bleomycin exposure and protection from lung injury in double receptor knockout mice," Am. J. Respir. Cell Mol. Biol., vol. 20, pp. 825-833, 1999.
43. C. C. Chang and C. J. Lin. LIBSVM: A library for support vector machines. Software [Online]. Available: http://www.csie.ntu.edu.tw/-cjlinlibsvm
44. X. Z. Lin, Y N. Sun, M. H. Horng, and X. Z. Guo, "Computer morphometry for liver fibrosis using an automatic image analysis system," Eng. Med. Biol. Soc., vol. 2, pp. 682-683, 1996.
45. R. Blechschmidt, R. Werthschutzky, and U. Lorcher, "Automated CT image evaluation of the lung: A morphology-based concept," IEEE Trans. Med. Imag., vol. 20, no. 5, pp. 434-442, May 2001.
46. L. Hsu and D. Schimel, "Computed tomography imaging of lungs in mouse models of human disease: Advancing the computing interfaces with physiology," in Proc. 17th IEEE Symp. Comput.-Based Med. Syst. (CBMS 2004), pp. 385-390.
47. Y. Chang, P. C. Chung, and P. H. Lai, "Using a spatiotemporal neural network on dynamic gadolinium-enhanced MR images for diagnosing recurrent nasal papilloma," IEEE Trans. Nucl. Sci., vol. 49, no. 1, pp. 225-238, Feb. 2002.
48. I. Maglogiannis and E. Zafiropoulos, "Utilizing support vector machines for the characterization of digital medical images," BMC Med. Inf. Decis. Making, vol. 4, no. 4, 2004.