Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function
Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect t...
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| Published in | Journal of signal processing systems Vol. 55; no. 1-3; pp. 77 - 90 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.04.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1939-8018 1939-8115 |
| DOI | 10.1007/s11265-008-0209-3 |
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| Abstract | Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley’s
K
function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image. |
|---|---|
| AbstractList | Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley's K function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image. Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley’s K function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image. |
| Author | de Paiva, Anselmo Cardoso de Oliveira Martins, Leonardo Gattass, Marcelo Silva, Aristófanes Corrêa |
| Author_xml | – sequence: 1 givenname: Leonardo surname: de Oliveira Martins fullname: de Oliveira Martins, Leonardo email: leomartins82@gmail.com organization: Department of Electrical Engineering, Federal University of Maranhão—UFMA – sequence: 2 givenname: Aristófanes Corrêa surname: Silva fullname: Silva, Aristófanes Corrêa organization: Department of Electrical Engineering, Federal University of Maranhão—UFMA – sequence: 3 givenname: Anselmo Cardoso surname: de Paiva fullname: de Paiva, Anselmo Cardoso organization: Department of Computer Science, Federal University of Maranhão—UFMA – sequence: 4 givenname: Marcelo surname: Gattass fullname: Gattass, Marcelo organization: Departament of Informatics, Pontifical Catholic University of Rio de Janeiro, Technical Scientific Center |
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| References | HaykinS.Redes Neurais: Princípios e Prática20012Porto AlegreBookman Urban, D. L. (2003). Spatial analysis in ecology—point pattern analysis. Available at: http://www.nicholas.duke.edu/lel/env352/ripley.pdf. 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. 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. BellottiR.de CarloF.TangaroS.GarganoG.MaggipintoG.CastellanoM.A completely automated CAD system for mass detection in a large mammographic databaseMedical Physics2006333066307510.1118/1.2214177 A.C.S. (2006). Learn about breast cancer. Available at http://www.cancer.org. MudigondaN. R.RangayyanR. M.DesautelsJ. E. L.Gradient and texture analysis for the classification of mammographic massesIEEE Transactions on Medical Imaging2000191032104310.1109/42.887618 ChanH.WeiJ.SahinerB.RaffertyE.WuT.RoubidoxM.Computer-aided detection system for breast masses on digital tomosynthesis mammograms: Preliminary experienceRadiology200523731075108010.1148/radiol.2373041657 KovalevV. A.KruggelF.GertzH. J.CramonD. Y. V.Three-dimensional texture analysis of MRI brain datasetsIEEE Transactions on Medical Imaging20012042443310.1109/42.925295 Azpitarte, R. L. (2006). Aportaciones al Diagnóstico de Cáncer Asistido por Ordenador. Tese de Doutorado. Universidad Politécnica de Valencia. 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. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Kluwer Academic Publishers. 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. MartinsL. de O.Braz JuniorG. S.SilvaE. C.SilvaA. C.PaivaA. C.Classification of breast tissues in mammogram images using Ripley’s K function and support vector machineICIAR20072007899910 Meyer-Baese, A. (2003). Pattern recognition for medical imaging. Elsevier. 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. Papoulis, A., & Pillai., S. U. (2002). Probability, random variables and stochastic processes. (4th edn.). McGraw-Hill. RipleyB. D.Modelling spatial patternsJournal of the Royal Statistical Society, B197739172212488279 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). University of Guelph. (2001). Shape Analysis & Measurement, CIS*6320, Lecture Notes. Li, X. (2001). Texture analysis for optical coherence tomography image. Master’s thesis, The University of Arizona. ScheuerellM. D.Quantifying aggregation and association in three dimensional landscapesEcology200485233223410.1890/03-0280 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. 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. GonzalezR. C.WoodsR. E.Digital image processing19923Reading, MAAddison-Wesley Heath, M., Bowyer, K., & Kopans, D. (1998). Current status of the digital database for screening mammography. Digital Mammography (pp. 457–460). Kluwer. DaleM. R. T.DixonP.FortinM. J.LegendreP.MyersD. E.RosenbergM. S.Conceptual and mathematical relationships among methods for spatial analysisEcography20022555857710.1034/j.1600-0587.2002.250506.x FritzkeB.TesauroE. G.TouretzkyD. S.LeenE. T. K.A growing neural gas network learns topologiesAdvances in Neural Information Processing Systems 71995Cambridge MAMIT Press625632 Martinez, T., & Schulten, K. (1991). A neural gas network learns topologies. Artificial Neural Networks. Elsevier. 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. MartinsL. de O.SilvaE. C.SilvaA. C.PaivaA. C.GattassM.Classification of breast masses in mammogram images using Ripley’s K function and support vector machineMLDM20072007784794 209_CR9 N. R. Mudigonda (209_CR21) 2000; 19 M. R. T. Dale (209_CR25) 2002; 25 209_CR6 209_CR5 209_CR4 209_CR3 209_CR14 209_CR15 209_CR30 209_CR31 H. Chan (209_CR8) 2005; 237 L. de O. Martins (209_CR26) 2007; 2007 209_CR16 209_CR18 B. D. Ripley (209_CR12) 1977; 39 B. Fritzke (209_CR11) 1995 R. Bellotti (209_CR7) 2006; 33 M. D. Scheuerell (209_CR22) 2004; 85 L. de O. Martins (209_CR10) 2007; 2007 R. C. Gonzalez (209_CR17) 1992 209_CR2 209_CR23 209_CR1 209_CR24 V. A. Kovalev (209_CR19) 2001; 20 S. Haykin (209_CR13) 2001 209_CR20 209_CR27 209_CR28 209_CR29 |
| References_xml | – reference: ChanH.WeiJ.SahinerB.RaffertyE.WuT.RoubidoxM.Computer-aided detection system for breast masses on digital tomosynthesis mammograms: Preliminary experienceRadiology200523731075108010.1148/radiol.2373041657 – reference: 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. – reference: Martinez, T., & Schulten, K. (1991). A neural gas network learns topologies. Artificial Neural Networks. Elsevier. – reference: GonzalezR. C.WoodsR. E.Digital image processing19923Reading, MAAddison-Wesley – reference: University of Guelph. (2001). Shape Analysis & Measurement, CIS*6320, Lecture Notes. – reference: Li, X. (2001). Texture analysis for optical coherence tomography image. Master’s thesis, The University of Arizona. – reference: FritzkeB.TesauroE. G.TouretzkyD. S.LeenE. T. K.A growing neural gas network learns topologiesAdvances in Neural Information Processing Systems 71995Cambridge MAMIT Press625632 – reference: KovalevV. A.KruggelF.GertzH. J.CramonD. Y. V.Three-dimensional texture analysis of MRI brain datasetsIEEE Transactions on Medical Imaging20012042443310.1109/42.925295 – reference: Heath, M., Bowyer, K., & Kopans, D. (1998). Current status of the digital database for screening mammography. Digital Mammography (pp. 457–460). Kluwer. – reference: MudigondaN. R.RangayyanR. M.DesautelsJ. E. L.Gradient and texture analysis for the classification of mammographic massesIEEE Transactions on Medical Imaging2000191032104310.1109/42.887618 – reference: 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. – reference: Urban, D. L. (2003). Spatial analysis in ecology—point pattern analysis. Available at: http://www.nicholas.duke.edu/lel/env352/ripley.pdf. – reference: Azpitarte, R. L. (2006). Aportaciones al Diagnóstico de Cáncer Asistido por Ordenador. Tese de Doutorado. Universidad Politécnica de Valencia. – reference: 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. – reference: 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). – reference: BellottiR.de CarloF.TangaroS.GarganoG.MaggipintoG.CastellanoM.A completely automated CAD system for mass detection in a large mammographic databaseMedical Physics2006333066307510.1118/1.2214177 – reference: Papoulis, A., & Pillai., S. U. (2002). Probability, random variables and stochastic processes. (4th edn.). McGraw-Hill. – reference: 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. – reference: A.C.S. (2006). Learn about breast cancer. Available at http://www.cancer.org. – reference: 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. – reference: 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. – reference: 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. – reference: MartinsL. de O.SilvaE. C.SilvaA. C.PaivaA. C.GattassM.Classification of breast masses in mammogram images using Ripley’s K function and support vector machineMLDM20072007784794 – reference: ScheuerellM. D.Quantifying aggregation and association in three dimensional landscapesEcology200485233223410.1890/03-0280 – reference: Meyer-Baese, A. (2003). Pattern recognition for medical imaging. Elsevier. – reference: 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. – reference: RipleyB. D.Modelling spatial patternsJournal of the Royal Statistical Society, B197739172212488279 – reference: HaykinS.Redes Neurais: Princípios e Prática20012Porto AlegreBookman – reference: Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Kluwer Academic Publishers. – reference: DaleM. R. T.DixonP.FortinM. J.LegendreP.MyersD. E.RosenbergM. S.Conceptual and mathematical relationships among methods for spatial analysisEcography20022555857710.1034/j.1600-0587.2002.250506.x – reference: MartinsL. de O.Braz JuniorG. S.SilvaE. C.SilvaA. C.PaivaA. C.Classification of breast tissues in mammogram images using Ripley’s K function and support vector machineICIAR20072007899910 – ident: 209_CR3 – ident: 209_CR1 – volume: 33 start-page: 3066 year: 2006 ident: 209_CR7 publication-title: Medical Physics doi: 10.1118/1.2214177 – volume: 85 start-page: 2332 year: 2004 ident: 209_CR22 publication-title: Ecology doi: 10.1890/03-0280 – ident: 209_CR28 doi: 10.1109/42.650876 – start-page: 625 volume-title: Advances in Neural Information Processing Systems 7 year: 1995 ident: 209_CR11 – volume: 19 start-page: 1032 year: 2000 ident: 209_CR21 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/42.887618 – ident: 209_CR20 – ident: 209_CR18 – ident: 209_CR16 – ident: 209_CR15 doi: 10.1007/978-94-011-5318-8_75 – ident: 209_CR24 – ident: 209_CR9 – ident: 209_CR14 – volume-title: Redes Neurais: Princípios e Prática year: 2001 ident: 209_CR13 – ident: 209_CR5 doi: 10.1016/B978-012119792-6/50130-3 – ident: 209_CR4 – ident: 209_CR2 – ident: 209_CR29 – volume: 20 start-page: 424 year: 2001 ident: 209_CR19 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/42.925295 – volume: 237 start-page: 1075 issue: 3 year: 2005 ident: 209_CR8 publication-title: Radiology doi: 10.1148/radiol.2373041657 – volume: 39 start-page: 172 year: 1977 ident: 209_CR12 publication-title: Journal of the Royal Statistical Society, B doi: 10.1111/j.2517-6161.1977.tb01615.x – volume: 2007 start-page: 899 year: 2007 ident: 209_CR26 publication-title: ICIAR – ident: 209_CR31 – ident: 209_CR30 doi: 10.4995/Thesis/10251/1862 – volume-title: Digital image processing year: 1992 ident: 209_CR17 – ident: 209_CR23 – volume: 25 start-page: 558577 year: 2002 ident: 209_CR25 publication-title: Ecography – ident: 209_CR27 – ident: 209_CR6 doi: 10.1109/CGIV.2005.30 – volume: 2007 start-page: 784 year: 2007 ident: 209_CR10 publication-title: MLDM |
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| Title | Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function |
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