Detection of breast masses in mammogram images using growing neural gas algorithm and Ripley's K function

Journal of Signal Processing Systems

Volumn 55, Issue 1-3, 2009, Pages 77-90

  De Oliveira Martins, Leonardo ^a   Silva, Aristófanes Corrêa ^a   De Paiva, Anselmo Cardoso ^a   Gattass, Marcelo ^b  

^a FEDERAL UNIVERSITY OF MARANHÃO   (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Computer aided detection; Growing neural gas; Mammography; Ripley's K function; Texture

Indexed keywords

BREAST CANCERS; BREAST MASS; COMPUTER-AIDED DETECTION; DIGITIZED MAMMOGRAMS; FALSE POSITIVES; GROWING NEURAL GAS; HEALTH CARE PROFESSIONALS; PUBLIC HEALTHS; RIPLEY'S K FUNCTION; SEGMENTED STRUCTURES; SUPPORT VECTORS;

DIAGNOSTIC RADIOGRAPHY; GASES; HEALTH CARE; IMAGE PROCESSING; MAMMOGRAPHY; MEDICAL COMPUTING; SUPPORT VECTOR MACHINES;

TEXTURES;

EID: 62449166341     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-008-0209-3     Document Type: Article

References (31)

1. A.C.S. (2006). Learn about breast cancer. Available at http://www.cancer.org.
2. Instituto Nacional do Câncer-INCA. (2006). Estimativa 2006: Incidência de Câncer no Brasil. Rio de Janeiro: INCA, 2005. Available at http://www.inca.gov.br/estimativa/2006/versaofinal.pdf. Accessed in 19 feb.
3. Instituto Nacional do Câncer-INCA. (2006). Câncer no Brasil: dados dos registros de base populacional, volume 3. Rio de Janeiro: INCA, 2003. Available at http://www.inca.gov.br/regpop/2003/versaofinal.pdf. Accessed in 15 sep.
4. Silva, D. F. (2006). Câncer de mama em mulheres no Maranhão: estudo de sobrevida no centro de assistência de Alta Complexidade em oncologia (CACON) em São Luís. Masters dissertation (Masters in Health Sciences). Federal University of Maranhão.
5. Sampat, P., Markey, M. K., & Bovik, A. C. (2005). Computer-aided detection and diagnosis in mammography. Computer-Aided Detection and Diagnosis in Mammography. Handbook of Image and Video Processing, 2ed.
6. Rezai-Rad, G., & Jamarani, S. (2005). Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network, cgiv, p. 197-201, International Conference on Computer Graphics, Imaging and Visualization (CGIV'05).
7. R. Bellotti F. de Carlo S. Tangaro G. Gargano G. Maggipinto M. Castellano 2006 A completely automated CAD system for mass detection in a large mammographic database Medical Physics 33 3066 3075
8. H. Chan J. Wei B. Sahiner E. Rafferty T. Wu M. Roubidox 2005 Computer-aided detection system for breast masses on digital tomosynthesis mammograms: Preliminary experience Radiology 237 3 1075 1080
9. Braz Junior, G., Silva, E. C., de Paiva, A. C., Silva, A. C., & Gattass, M. (2007). Breast Tissues Mammograms Images Classification using Moran's Index, Geary's Coefficient and SVM. In: 14th International Conference on Neural Information Processing (ICONIP 2007), 2007, Kitakyushu. Lecture Notes Computer Science-LNCS.
10. L. de O. Martins E. C. Silva A. C. Silva A. C. Paiva M. Gattass 2007 Classification of breast masses in mammogram images using Ripley's K function and support vector machine MLDM 2007 784 794
11. Fritzke, B. (1995). A growing neural gas network learns topologies. In E. G. Tesauro, D. S. Touretzky, & E. T. K. Leen (Eds.), Advances in Neural Information Processing Systems 7 (pp. 625-632). Cambridge MA: MIT Press.
12. B. D. Ripley 1977 Modelling spatial patterns Journal of the Royal Statistical Society, B 39 172 212
13. Haykin, S. (2001). Redes Neurais: Princípios e Prática (2nd ed.). Porto Alegre: Bookman.
14. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Kluwer Academic Publishers.
15. Heath, M., Bowyer, K., & Kopans, D. (1998). Current status of the digital database for screening mammography. Digital Mammography (pp. 457-460). Kluwer.
16. Martinez, T., & Schulten, K. (1991). A neural gas network learns topologies. Artificial Neural Networks. Elsevier.
17. Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing (3rd ed.). Reading, MA: Addison-Wesley.
18. Meyer-Baese, A. (2003). Pattern recognition for medical imaging. Elsevier.
19. V. A. Kovalev F. Kruggel H. J. Gertz D. Y. V. Cramon 2001 Three-dimensional texture analysis of MRI brain datasets IEEE Transactions on Medical Imaging 20 424 433
20. Li, X. (2001). Texture analysis for optical coherence tomography image. Master's thesis, The University of Arizona.
21. N. R. Mudigonda R. M. Rangayyan J. E. L. Desautels 2000 Gradient and texture analysis for the classification of mammographic masses IEEE Transactions on Medical Imaging 19 1032 1043
22. M. D. Scheuerell 2004 Quantifying aggregation and association in three dimensional landscapes Ecology 85 2332 234
23. Urban, D. L. (2003). Spatial analysis in ecology-point pattern analysis. Available at: http://www.nicholas.duke.edu/lel/env352/ripley.pdf.
24. Papoulis, A., & Pillai., S. U. (2002). Probability, random variables and stochastic processes. (4th edn.). McGraw-Hill.
25. M. R. T. Dale P. Dixon M. J. Fortin P. Legendre D. E. Myers M. S. Rosenberg 2002 Conceptual and mathematical relationships among methods for spatial analysis Ecography 25 558577
26. L. de O. Martins G. S. Braz Junior E. C. Silva A. C. Silva A. C. Paiva 2007 Classification of breast tissues in mammogram images using Ripley's K function and support vector machine ICIAR 2007 899 910
27. University of Guelph. (2001). Shape Analysis & Measurement, CIS*6320, Lecture Notes.
28. Rangayyan, R. M., El-Faramawy, N. M., Desautels, J. E. L., & Alim, O. A. (1997). Measures of acutance and shape for classification of breast tumors, Associate Member, IEEE.
29. Sousa, J. R. F. S., Silva, A. C., & Paiva, A. C. (2007). Lung Structures Classification Using 3D Geometric Measurements and SVM. In: 12th Iberoamerican Congress on Pattern Recognition-CIARP 2007, 2007, Valparaiso. Lecture Notes Computer Science-LNCS. (pp. 783-792). Berlin: Springer-Verlag, v. 4756.
30. Azpitarte, R. L. (2006). Aportaciones al Diagnóstico de Cáncer Asistido por Ordenador. Tese de Doutorado. Universidad Politécnica de Valencia.
31. Chang, C.-C., & Lin, C.-J. (2007). LIBSVM: a Library for Support Vector Machines. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm. pdf. Accessed in 08 oct.