A fuzzy-based learning vector quantization neural network for recurrent nasal papilloma detection

IEEE Transactions on Circuits and Systems I: Regular Papers

Volumn 54, Issue 12, 2007, Pages 2619-2627

  Chang, Chuan Yu ^a   Zhuang, Da Feng ^b  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b CHROMA ATE INC   (Taiwan)

Author keywords

Fuzzy c means; Learning vector quantization; Medical image processing; Neural network; Recurrent nasal papilloma (RNP)

Indexed keywords

DIAGNOSIS; FUZZY INFERENCE; FUZZY SETS; GADOLINIUM; IMAGE ENHANCEMENT; LEARNING ALGORITHMS; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGE PROCESSING; MEDICAL IMAGING; NEURAL NETWORKS; TUMORS; VECTOR QUANTIZATION;

CLINICAL DIAGNOSIS; COMPLETE SOLUTIONS; DYNAMIC MAGNETIC RESONANCE IMAGING (MRI); FUZZY C MEAN; LEARNING VECTOR QUANTIZATION; LEARNING VECTOR QUANTIZATION NEURAL NETWORKS; RECURRENT NASAL PAPILLOMA (RNP); REGION GROWING METHODS;

RECURRENT NEURAL NETWORKS;

EID: 64849091416     PISSN: 10577122     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSI.2007.906061     Document Type: Article

References (22)

1. M. Muller-Schimpfle, G. Brix, G. Layer, P. Schlag, R. Engenhart, S. Frohmuller, T. Hess, I. Zuna, W. Semmler, and G. van Kaick, "Recurrent rectal cancer diagnosis with dynamic MR image," Radiolog., vol. 189, pp. 881-889, 1993.
2. G. Brix, W. Semmler, R. Schad, G. Layer, and W. J. Lorenz, "Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR images," J. Comput. Assist. Tomog., vol. 15, pp. 621-628, 1991.
3. C.-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, pp. 225-238, Feb. 2002.
4. N. B. Karayiannis, "A methodology for constructing fuzzy algorithms for learning vector quantization," IEEE Trans. Neural Netw., vol. 8, pt. 5, pp. 505-518, May 1997.
5. N. B. Karayiannis and P.-I. Pai, "Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization," IEEE Trans. Med. Imag., vol. 18, no. 2, pp. 172-180, Feb. 1999.
6. J.-S. Lin, K.-S. Cheng, and C.-W. Mao, "A fuzzy Hopfield neural network for medical image segmentation," IEEE Trans. Nucl. Sci., vol. 43, no. 8, pp. 2389-2398, Aug. 1996.
7. N. B. Karayiannis and J. C. Bezdek, "An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering," IEEE Trans. Fuzzy Syst., vol. 5, pp. 622-628, Nov. 1997.
8. W. C. Huang, C. C. Hsu, C. Lee, and P. H. Lai, "Recurrent nasal Tumor Detection by Dynamic MRI," IEEE Eng. Med. Biol., pp. 100-105, Jul./Aug. 1999.
9. C.-Y. Chang, P.-C. Chung, W.-C. Huang, and P.-H. Lai, "An eigensupport difference operator to fuse CT and MR images for nasopharyngeal carcinoma diagnosis," Biomed. Eng. Appl., Basis & Comm., vol. 12, pp. 101-109, 2000.
10. G. P. Manuel, J. C. Maria, M. Antonio, and D. Cabello, "Computeraided diagnosis: A neural- network-based approach to lung nodule detection," IEEE Trans. Med. Imag., vol. 17, pp. 872-880, 1998.
11. S. Yu and L. Guan, "A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films," IEEE Trans. Med. Imag., vol. 19, no. 2, pp. 115-126, Feb. 2000.
12. T. Pappas, "An adaptive clustering algorithm for image segmentation," IEEE Trans. Signal Process., vol. 40, no. 7, pp. 901-914, Jul. 1992.
13. U. Schmiedl, D. A. Ortendahl, A. S. Mark, I. Berry, and L. Kaufman, "The utility of principal component analysis for the image display of brain lesions. A preliminary, comparative study," Med. Reg. Med, vol. 4, pp. 471-486, 1987.
14. D. A. Ortendahl, "The application of principal component analysis to multivariate MRI data," in IEEE 8th Annu. Conf. Eng. Med. Biol. Soc. 1986, pp. 1065-1068.
15. M. Unser and M. Eden, "Weighted averaging of a set of noisy images for maximum signal-to-noise ratio," IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 5, pp. 890-895, May 1990.
16. J. W. V. Miller, J. P. Windham, and S. CKwatra, "Optimal filtering of radiographic image sequences using simultaneous diagonalization," IEEE Trans. Med. Image, vol. MI-3, no. 1, pp. 116-123, Jan. 1984.
17. J. P. Windham, M. A. Abd Allah, D. A. Reimaun, J. W. Froelich, and A. M. Haggar, "Eigenimage filtering in MR imaging," J. Comput. Ass. Tomogr., vol. 12, pp. 1-9, 1988.
18. F. Ebner, H. Y. Kressel, and M. C. Mintz et al., "Tumor recurrence versus fibrosis in the female pelvis: Differentiation with MRI at 1.5 T," Radiol., vol. 166, pp. 333-340, 1988.
19. A. A. Mancuso, R. R. Million and N. J. Cassisi, Eds., "Imaging in patients with head and neck cancer," in in Management of Head and Neck Cancer: A Multidisciplinary Approach. Philadelphia, PA: Lippincott, 1994, pp. 43-59.
20. M. J. P. Castanho, L. C. Barros, A. Yamakami, and L. L. Vendite, "Fuzzy receiver operating characteristic curve: An option to evaluate diagnostic tests," IEEE Trans. Inf. Tech. Biomed., vol. 11, no. 3, pp. 244-250, 2007.
21. A. P. Dhawan, Medical Image Analysis. New York: IEEE Press, 2003.
22. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition, A survey of fuzzy clustering algorithms for pattern recognition. II," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 6, pp. 786-801, Jun. 1999.