Decision system based on fuzzy logic for detection of architectural distortion
Keywords:
Breast cancer, Architectural distortion, Mammography, Digital image processing, Computer aided diagnosis
Abstract
Architectural distortion is an abnormal change in the mammary gland tissue with the consequent formation of thin and speculated lesions that are not associated with the presence of a mass. It is the third most common mammographic finding and because of its subtlety it is the first cause of false-negative findings on screening mammograms.This paper presents the design, implementation and test of a new method that serves as support for the detection of architectural distortion in the mammary gland from breast radiology images. The method proposed here assists the specialists in the diagnosis of breast cancer through four main phases,which encompass from the preprocessing to the classification of regions of interest using a classifier based on fuzzy logic. The method described in this paper was validated through the analysis of mammographic images from DDSM (Digital Database for Screening Mammography) obtaining values of 90.7% in the overall accuracy.This result is a very important contribution and encourages the research in order to reduce the high number of misdiagnoses that are currently presented and lead to the high rates of morbidity from breast cancer.
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References
[1] E. Coto, “Método de Segmentación de Imágenes Médicas,” 2003.
[2] Instituto Nacional de Cancerología, “Casos nuevos de cáncer de mama, según estadio clínico al ingreso y régimen de afiliación.” 2009.
[3] F. R. Narváez E., “Sistema de Anotación para Apoyo en el Seguimiento y Diagnóstico de Cáncer de Seno,” Universidad Nacional de Colombia, 2010.
[4] American College of Radiology (ACR), Breast Imaging Reporting and Data System, 4th ed. 2003.
[5] A. M. Knutzen and J. J. Gisvold, “Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions.,” in Mayo Clinic proceedings. Mayo Clinic, 1993, Vol. 68, p. 454.
[6] S. Banik, R. M. Rangayyan, and J. E. L. Desautels, “Detection of Architectural Distortion in Prior Mammograms,” Medical Imaging, IEEE Transactions on, vol. 30, no. 2, pp. 279–294, 2011.
[7] D. A. Gómez Betancur, “Método de detección de distorsiones de la arquitectura de la glándula mamaria a partir de imágenes radiológicas,” Universidad Nacional de Colombia, 2012.
[8] M. D. Phillips, “Invasive Lobular Breast Carcinoma: Pathology And Genetics Reflected By MRI,” The WorldCare Clinical (WCC) Note, Vol. 4, 2010.
[9] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase, “Novel method for detecting mammographic architectural distortion based on concentration of mammary gland,” in International Congress Series, 2004, vol. 1268, pp. 867–871.
[10] B. C. Yankaskas, M. J. Schell, R. E. Bird, and D. A. Desrochers, “Reassessment of breast cancers missed during routine screening mammography: a communitybased study,” American Journal of Roentgenology, vol. 177, no. 3, p. 535, 2001.
[11] M. Bustamante, G. Lefranc, A. Núñez, and M. G. Pesce, “Calculo De La Amplitud Dispersada En Mamografias, Usando Como Modelo De Degradacion El Filtro Bosso.,” PHAROS, Vol. 8, No. 1, 2001.
[12] H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai, and H. N. Du, “Approaches for automated detection and classification of masses in mammograms,” Pattern Recognition, vol. 39, no. 4, pp. 646–668, 2006.
[13] J. Tang, R. M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances,” Information Technology in Biomedicine, IEEE Transactions on, Vol. 13, No. 2, pp. 236–251, 2009.
[14] R. M. Rangayyan and T. M. Nguyen, “Fractal Analysis of Contours of Breast Masses in Mammograms,” Journal of Digital Imaging, Vol. 20, pp. 223–237, Oct. 2006.
[15] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase, “Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms,” International Congress Series, vol. 1256, no. 0, pp. 950–955, Jun. 2003.
[16] N. Eltonsy, G. D. Tourassi, and A. Elmaghraby, “Investigating performance of a morphology-based CAD scheme in detecting architectural distortion in screening mammograms,” Proc. 20th Int. Congr. Exhib. Comput. Assist. Radiol. Surg, pp. 336–338, 2006.
[17] G. D. Tourassi, D. M. Delong, and C. E. Floyd, “A study on the computerized fractal analysis of architectural distortion in screening mammograms,” Physics in Medicine and Biology, Vol. 51, pp. 1299–1312, Mar. 2006.
[18] R. M. Rangayyan, S. Prajna, F. J. Ayres, and J. E. L. Desautels, “Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis,” International Journal of Computer Assisted Radiology and Surgery, Vol. 2, No. 6, pp. 347–361, 2008.
[19] S. Prajna, R. M. Rangayyan, F. J. Ayres, and J. E. L. Desautels, “Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis,” in Proceedings of SPIE, 2008, Vol. 6915, p. 691529.
[20] R. M. Rangayyan, S. Banik, and J. E. L. Desautels, “Computer-aided detection of architectural distortion in prior mammograms of interval cancer,” Journal of Digital Imaging, Vol. 23, No. 5, pp. 611–631, 2010.
[21] S. Russell, Inteligencia Artificial - Un Enfoque Moderno, 2nd ed. 2004.
[22] T. Nakashima, G. Schaefer, Y. Yokota, and H. Ishibuchi, “A weighted fuzzy classifier and its application to image processing tasks,” Fuzzy sets and systems, Vol. 158, No. 3, pp. 284–294, 2007.
[23] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” Fuzzy Systems, IEEE Transactions on, Vol. 9, No. 4, pp. 506– 515, 2001.
[24] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, No. 6, pp. 610–621, Nov. 1973.
[25] M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 5, pp. 1264–1274, Oct. 1989.
[26] H. Tamura, S. Mori, and T. Yamawaki, “Textural Features Corresponding to Visual Perception,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 460–473, Jun. 1978.
[27] D.-Y. Tsai and K. Kojima, “Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy,” 2005.
[2] Instituto Nacional de Cancerología, “Casos nuevos de cáncer de mama, según estadio clínico al ingreso y régimen de afiliación.” 2009.
[3] F. R. Narváez E., “Sistema de Anotación para Apoyo en el Seguimiento y Diagnóstico de Cáncer de Seno,” Universidad Nacional de Colombia, 2010.
[4] American College of Radiology (ACR), Breast Imaging Reporting and Data System, 4th ed. 2003.
[5] A. M. Knutzen and J. J. Gisvold, “Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions.,” in Mayo Clinic proceedings. Mayo Clinic, 1993, Vol. 68, p. 454.
[6] S. Banik, R. M. Rangayyan, and J. E. L. Desautels, “Detection of Architectural Distortion in Prior Mammograms,” Medical Imaging, IEEE Transactions on, vol. 30, no. 2, pp. 279–294, 2011.
[7] D. A. Gómez Betancur, “Método de detección de distorsiones de la arquitectura de la glándula mamaria a partir de imágenes radiológicas,” Universidad Nacional de Colombia, 2012.
[8] M. D. Phillips, “Invasive Lobular Breast Carcinoma: Pathology And Genetics Reflected By MRI,” The WorldCare Clinical (WCC) Note, Vol. 4, 2010.
[9] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase, “Novel method for detecting mammographic architectural distortion based on concentration of mammary gland,” in International Congress Series, 2004, vol. 1268, pp. 867–871.
[10] B. C. Yankaskas, M. J. Schell, R. E. Bird, and D. A. Desrochers, “Reassessment of breast cancers missed during routine screening mammography: a communitybased study,” American Journal of Roentgenology, vol. 177, no. 3, p. 535, 2001.
[11] M. Bustamante, G. Lefranc, A. Núñez, and M. G. Pesce, “Calculo De La Amplitud Dispersada En Mamografias, Usando Como Modelo De Degradacion El Filtro Bosso.,” PHAROS, Vol. 8, No. 1, 2001.
[12] H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai, and H. N. Du, “Approaches for automated detection and classification of masses in mammograms,” Pattern Recognition, vol. 39, no. 4, pp. 646–668, 2006.
[13] J. Tang, R. M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances,” Information Technology in Biomedicine, IEEE Transactions on, Vol. 13, No. 2, pp. 236–251, 2009.
[14] R. M. Rangayyan and T. M. Nguyen, “Fractal Analysis of Contours of Breast Masses in Mammograms,” Journal of Digital Imaging, Vol. 20, pp. 223–237, Oct. 2006.
[15] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo, and T. Iwase, “Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms,” International Congress Series, vol. 1256, no. 0, pp. 950–955, Jun. 2003.
[16] N. Eltonsy, G. D. Tourassi, and A. Elmaghraby, “Investigating performance of a morphology-based CAD scheme in detecting architectural distortion in screening mammograms,” Proc. 20th Int. Congr. Exhib. Comput. Assist. Radiol. Surg, pp. 336–338, 2006.
[17] G. D. Tourassi, D. M. Delong, and C. E. Floyd, “A study on the computerized fractal analysis of architectural distortion in screening mammograms,” Physics in Medicine and Biology, Vol. 51, pp. 1299–1312, Mar. 2006.
[18] R. M. Rangayyan, S. Prajna, F. J. Ayres, and J. E. L. Desautels, “Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis,” International Journal of Computer Assisted Radiology and Surgery, Vol. 2, No. 6, pp. 347–361, 2008.
[19] S. Prajna, R. M. Rangayyan, F. J. Ayres, and J. E. L. Desautels, “Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis,” in Proceedings of SPIE, 2008, Vol. 6915, p. 691529.
[20] R. M. Rangayyan, S. Banik, and J. E. L. Desautels, “Computer-aided detection of architectural distortion in prior mammograms of interval cancer,” Journal of Digital Imaging, Vol. 23, No. 5, pp. 611–631, 2010.
[21] S. Russell, Inteligencia Artificial - Un Enfoque Moderno, 2nd ed. 2004.
[22] T. Nakashima, G. Schaefer, Y. Yokota, and H. Ishibuchi, “A weighted fuzzy classifier and its application to image processing tasks,” Fuzzy sets and systems, Vol. 158, No. 3, pp. 284–294, 2007.
[23] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” Fuzzy Systems, IEEE Transactions on, Vol. 9, No. 4, pp. 506– 515, 2001.
[24] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, No. 6, pp. 610–621, Nov. 1973.
[25] M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 5, pp. 1264–1274, Oct. 1989.
[26] H. Tamura, S. Mori, and T. Yamawaki, “Textural Features Corresponding to Visual Perception,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 460–473, Jun. 1978.
[27] D.-Y. Tsai and K. Kojima, “Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy,” 2005.
Published
2012-07-01
How to Cite
Gómez Betancur, D., & Branch Bedoya, J. (2012). Decision system based on fuzzy logic for detection of architectural distortion. ITECKNE, 9(2), 118-127. https://doi.org/https://doi.org/10.15332/iteckne.v9i2.2764
Issue
Section
Research and Innovation Articles
