Feature extraction using composite individual genetic programming: An application to mass classification

Applied Mechanics and Materials

Volumn 198-199, Issue , 2012, Pages 468-473

  Lv, Yinghua ^a   Guo, Yuting ^b   Sun, Hui ^a   Zhang, Ming ^b   Wang, Jianzhong ^a,b  

^a NORTHEAST NORMAL UNIVERSITY   (China)

^b JILIN UNIVERSITY   (China)

Author keywords

Feature extraction; Genetic programming; Mammogram; Mass

Indexed keywords

BREAST CANCER DIAGNOSIS; INPUT FEATURES; MAMMOGRAM; MAMMOGRAPHIC IMAGES; MASS;

FEATURE EXTRACTION; MAMMOGRAPHY; X RAY SCREENS;

GENETIC PROGRAMMING;

EID: 84869228890     PISSN: 16609336     EISSN: 16627482     Source Type: Book Series    
DOI: 10.4028/www.scientific.net/AMM.198-199.468     Document Type: Conference Paper

References (24)

1. Cancer Research UK. Breast cancer factsheet; February 2004.
2. National Cancer Institute of Canada.Canadian cancer statistics 2006; 2006.
3. Alfonso Rojas Dominguez, Asoke K, Nandi Toward breast cancer diagnosis based on automated segmentation ofmasses inmammograms, Pattern Recognition 42 (2009),1138-1148.
4. Encarnacao JL, Peitgen H-O, Sakas G, Englert G. Fractal geometry and computer graphics. Berlin: Springer-Verlag;1992.
5. Caldwell CB, Stapleton SJ, Holdsworth DW, Jong RA, Weiser WJ, Cooke G, et al. Characterization of mammographic parenchymal pattern by fractal dimension. Phys Med Biol,1990;35:235-47.
6. Bruce LM, Adhami RR. Classifying mammographic mass shapes using the wavelet transform modulus-maxima method. IEEE Trans Med Imag 1999;18(12):1170-7.
7. Homer MJ. Imaging features and management of characteristically benign and probably benign lesions. Radiol Clin N Am 1987;25(5):939-51.
8. Homer MJ. Breast imaging: pitfalls, controversies and some practical thoughts. Radiol Clin N Am 1985;23(3):459-72.
9. Mavroforakis M, Georgiou H, Dimitropoulos N, Cavouras D, Theodoridis S. Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines. Eur J Radiol 2005;54:80-9.
10. Z.M. Huo, M.L. Giger, C.J. Vyborny, D.E. Wolverton, R.A. Schmidt, K. Doi, Automatedcomputerized classification of malignant and benign masses on digitized mammograms, Acad. Radiol. 5 (1998)155-168.
11. N. Petrick, H.P. Chan, B. Sahiner, M.A. Helvie, Combined adaptiveenhancement and region-growing segmentation of breast masses on digitized mammograms, Med. Phys. 26 (1999) 1642-1654.
12. N.M. El-Faramawy, R.M. Rangayyan, J.E.L. Desautels, O.A. Alim, Shape factors for analysis of breast tumors in mammograms, 1996 Canadian Conference on Electrical and Computer Engineering, 1996, pp. 355-358.
13. N. Petrick, H.P. Chan, B. Sahiner, M.A. Helvie, Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms, Med. Phys. 26 (1999) 1642-1654.
14. B. Sahiner, H.P. Chan, N. Petrick, D. Wei, M.A. Helvie, D.D. Adler, M.M. Goodsitt, Classification of mass and normal breast tissue:a convolution neural network classifier with spatial domain and texture images, IEEE Trans. Med. Imaging 15 (5) (1996) 598-609.
15. B. Sahiner, H.P. Chan, N. Petrick, M.A. Helvie, M.M. Goodsitt, Design of a high-sensitivity classifier based on a genetic algorithm:application to computer-aided diagnosis, Phys. Med. Biol. 43 (10)(1998) 2853-2871.
16. F.F. Yin, M.L. Giger, K. Doi, C.J. Vyborny, R.A. Schmidt, Computerized detection of masses in digital mammograms:investigation of feature-analysis techniques, J. Digital Imaging 7(1994) 18-26.
17. K. Bovis, S. Singh, J. Fieldsend, C. Pinder, Identification of masses in digital mammograms with MLP and RBF nets, in: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks Com, 2000, pp. 342-347.
18. B. Sahiner, N. Petrick, H.P. Chan, Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization, IEEE Trans. Med. Imaging 20 (12)(2001) 1275-1284.
19. B. Sahiner, H.P. Chan, N. Petrick, M.A. Helvie, M.M. Goodsitt, Design of a high-sensitivity classifier based on a genetic algorithm:application to computer-aided diagnosis, Phys. Med. Biol. 43 (10)(1998) 2853-2871.
20. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, 1992.
21. Koza, John R. Genetic Programming. Cambridge, MA: MIT Press, 1992.
22. J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.
23. Alfonso Rojas Dominguez, Asoke K. Nandi, Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection, Computerized Medical Imaging and Graphics 32 (2008) 304-315.
24. Pasquale Delogu, Maria Evelina Fantacci, Parnian Kasae, Alessandra Retico, Characterization of mammographicmasses using a gradient-based segmentation algorithmand a neural classifier, Computers in Biology and Medicine 37 (2007) 1479-1491.