Clustering and least square based neural technique for learning and identification of suspicious areas within digital mammograms

Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007

Volumn 1, Issue , 2008, Pages 190-194

  McLeod, Peter ^a   Verma, Brijesh ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); IMAGE PROCESSING; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS;

DIGITAL MAMMOGRAMS; SUSPICIOUS AREAS;

MAMMOGRAPHY;

EID: 41149120706     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIMA.2007.126     Document Type: Conference Paper

References (34)

1. Parkin, M., Freddie Bray, F, Ferlay, J. and Pisani, P. "Global Cancer Statistics", A Cancer Journal for Clinicians, Vol. 55, pp. 74-108, 2005, American Cancer Society
2. National Breast Cancer Centre, http://www.nbcc.org.au/bestpractice/ statistics/
3. Cheng, H. D., Cai, X., Chen, X., Hu, L., and Lou, X., "Computer-aided Detection and Classification of Microcalcification in Mammograms: a survey", Pattern Recognition,Vol. 36, pp. 2967-2991, 2003.
4. Verma, B. and Zakos, J., "A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques", IEEE Trans, on Info. Tech. in Biomedicine, Vol. 5, pp.46-54, 2001.
5. Zhang P., Verma, B., and Kumar K., "A Neural-Genetic Algorithm for Feature Selection and Breast Abnormality Classification in Digital Mammography", Proc. of IEEE-IJCNN, Vol. 3, pp. 2303-2309, 2004.
6. Zhang, P., Verma, B. and Kumar, K. Neural Vs. Statistical Classifier in Conjunction with Genetic Algorithm Feature Selection in Digital Mammography, Pattern Recognition Letters, 26(7), pp. 909-919, 2005.
7. Wu Y., He J., Man Y., and Arribas J. I., "Neural Network Fusion Strategies for Identifying Breast Masses", Proc. of IEEE-IJCNN, Vol. 3, pp. 2437-2442, 2004.
8. Bovis K., Singh S., Fieldsend J., and Pinder C., "Identification of Masses in Digital Mammograms with MLP and RBF Nets", Proc. of IEEE-IJCNN, Vol. 1, pp. 342-347, 2000.
9. Chitre Y., Dhawan A. P., and Moskowitz M., "Artificial Neural Network Based Classification of Mammographic Microcalcifications using Image Structure Features", State of The Art in Digital Mammographic Image Analysis, World Scientific Publication, Vol. 9, pp. 167-197, 1994.
10. Christoyianni L., Dermatas E., and Kokkinakis G., "Neural Classification of Abnormal Tissues in Digital Mammography using Statistical Features of the Texture", Proc. Of IEEE-ICECS, Vol. 1, pp. 117-120, 1999.
11. Cheng, H., Lui, Y. M., and Freimanis, R. I., "A Novel Approach to Microcalcification Detection using Fuzzy Logic Technique", IEEE Trans, on Medical Imaging, Vol. 17, pp. 442-450, 1998.
12. Yoshida H., Nishikawa R. N., Geiger M.L., and Doi. K., "Signal/Background Separation By Wavelet Packets for Detection of Microcalcifications in Mammograms", Proc. of SPLE on Wavelet Applications in Signal and Image Processing IV, Vol. 2825, pp 805-811, 1996.
13. Wang T. C., and Karayiannis N. B., "Detection of Microcalcification in Digital Mammograms using Wavelets", IEEE Transactions on Medical Imaging, Vol. 17, pp. 498-509, 1998.
14. Wroblewska A., Boninski P., Przelaskowski A., and Kazubek M., "Segmentation and feature extraction for reliable classification of microcalcification in digital mammograms", Opto-Electronics Review, Vol. 11, pp. 227-235, 2003.
15. Markey M. K., Lo J. Y., and Floyd C. E., "Differences between Computer-aided Diagnosis of Breast Masses and That of Calcifications", Radiology, Vol 223, pp. 489-493, 2002.
16. Yu S., and Guan L., "A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films", IEEE Trans, on Medical Imaging, Vol. 19, pp. 115-126, 2000.
17. Heath M., Bowyer K., Kopans D., Moore R., and Kegelmeyer Jr. P., "The Digital Database for Screening Mammography", IWDM-2000, Medical Physics Publishing, 2001.
18. Rangayyan R. M., Mudigonda N. R., and Leo Desautels J. E., "Boundary modeling and shape analysis methods for classification of mammographic masses", Medical and Biological Eng. and Computing, Vol. 38, pp. 487-496, 2000.
19. Verma, B. "Fast Training of Multilayer Perceptrons (MLPs)", IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1314-1321, 1997.
20. Verma, B. and Panchal, R. "Neural Networks for the Classification of Benign and Malignant Patterns in Digital Mammograms", Advances in Applied Artificial Intelligence, Idea Group, Inc., USA, Book Editor: John Fulcher, 2006.
21. Kallergi M, Carney GM and Gaviria J. "Evaluating the Performance of Detection Algorithms in Digital Mammography", Med Phys. 26(2):267-75, 1999.
22. Pickard, J. "Using Receiver Operating Characteristic (ROC) Curves to Evaluate Digital Mammography", 2005, http://ric.uthscsa.edu/personalpages/ lancaste/DI2_Projects_2004/JP_Project.pdf
23. Markey, M., Lo, J., Tourassi, G., and Floyd, C., "Self-organizing Map for Cluster Analysis of a Breast Cancer Database", Artificial Intelligence in Medicine, 27, pp. 113-127, 2003.
24. Lee, G., and Bottema, M., "Classification of Masses in Screening Mammograms as Benign or Malignant", IWDM2000, Canada, 2000, http://www.sunnybrook.on.ca/~iwdm2000/iwdm2000_ab stract_book.pdf, pp. 52.
25. Wu, Y., "Application of Neural Networks in Mammography", Radiology, vol. 187, pp. 81-87, 1993.
26. Bakic, P., and Barzakovic, D., "Application of Neural Networks in Computer Aided Diagnosis of Breast Cancer", Proceedings of 5th workshop on Neural Network Applications, NEUREL, Belgrade, Yugoslavia, 1997.
27. Guran, M., Chan, H., Sahiner, B., Hadjiiski, L., Petrick, N., and Helvie, M., "Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Microcalcifications", Acad. Radiology. 9 (4), pp. 420-29, 2002.
28. Guran, M., Sahiner, B., Chan, H., Hadjiiski, L., and Petrick, N., "Selection of an Optimal Neural Network Architecture for Computer-Aided Detection of Microcalcifications-Comparison of Automated Optimization Techniques", Med. Phys. 28 (9), pp. 1937-1948, 2001.
29. Papadopoulos A., Fotiadis D. I., and Likas A., "An Automatic Microcalcification Detection System Based on a Hybrid Neural Network Classifier", Artificial Intelligence in Medicine, Vol. 25, pp. 149-167, 2002.
30. Lee, Y., and Tsai, D., "Computerized Classification of Microcalcifications on Mammograms using Fuzzy Logic and Genetic Algorithm", Medical Imaging, Proc. of SPLE, Vol. 5370, pp 952-959, 2004.
31. Khuwaja, G. A., and Abu-Rezq, A. N., "Bi-Model Breast Cancer Classification System, Pattern Analysis Applications", Springer-Verlag, Vol. 7, pp. 235-242, 2004.
32. Lo, J. Y., Land W. H., Morrison, C. T, "Evolutionary Programming Technique for Reducing Complexity of Artificial Neural Networks for Breast Cancer Diagnosis", Proc. of SPLE 3979: 153-158, 2000.
33. AbuBaker, A., Qahwaji, R., Aqel, M., Al-Osta, H. and Saleh, M., "Efficient Preprocessing of USF and MLAS Mammogram Images", Journal of Computer Science, Vol. 3, No. 2, pp. 67-75, 2006.
34. Panchal, R. and Verma, B., Neural Classification of Mass Abnormalities with Different types of Features in Digital Mammography, International Journal of Computational Intelligence and Applications, Vol. 6, No. 1, pp. 61-76, 2006.