O uso de Equações de Difusão no Processo de Detecção de Regiões Suspeitas em Mamografias
Resumo
Neste trabalho é explorado o uso de um filtro de suavização anisotrópica via Equações Diferenciais Parciais na fase de pré-processamento para a detecção de regiões suspeitas em mamografias. O filtro de suavização é baseado em equações de difusão que preserva bordas ao mesmo tempo em que a imagem é suavizada nas regiões homogêneas. O método foi testado em 56 imagens da base de dados mini Mammographic Image Analysis Society (MIAS) e 30 imagens da base de dados Digital Database for Screening of Mammography (DDSM). O método foi avaliado em termos de número de regiões corretamente detectadas e da média de falso-positivos por imagem, tendo apresentado bons resultados.Referências
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R. M. Rangayyan, F. J. Ayres, and J. E. Leo, Desautels. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of The Franklin Institute, pp. 312–348, 2007.
M. L. Giger. Computer-aided diagnosis of breast lesions in medical images. IEEE Computer Society Press, pp. 39–45, Los Alamitos, CA, USA, 1995.
Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, and C. E. Metz. Computadorized classification of benign and malignant masses on digitized mammograms: A study of robustness. Academic Radiology, pp. 1077–1084. 2000.
Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz. Breast cancer: effectiveness of computer-aided diagnosis - observer study with independent database of mammograms. Academic Radiology, pp. 224–256. 2002.
S. Gupta, Chyn P.F., and M.K. Markey. Breast cancer cadx based on bi-rads descriptors from two mammographic views. Med. Phys, pp. 1810–1817. 2006.
E. D. C. Anderson, B. Muir, J. S. Walsh, and A. E. Kirkpatrick. The efficacy of double reading mammograms in breast screening. Clinical Radiology, pp. 248–251. 1994.
E. L. Thurfjell, K. A. Lernevall, and A. Taube. Benefit of independent double reading in a population-based mammography screening program. Radiological Society, pp. 241–244. 1994.
B. Verma and J. Zakos. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans. on Information Technology in Biomedicine, pp. 46–54, 2001.
J. A. Baker, E. L.Rosen, and J. Y. Lo. Computer-aided detection (cad) in screening mammography: Sensitivity of commercial cad systems for detection architectural distortion. American Journal of Roentgenology, pp. 1083–1088, 2003.
S. M. Astley and F. J. Gilbert. Computer-aided detection in mammography. Clinical Radiology, pp. 390–399. 2004.
N. Petrick, H. P. Chan, and B. Sahiner. An adaptive density-weighted contrast enhancement filter for mamographic breast mass detection. IEEE Trans. on Medical Imaging, pp. 59–67, 1996.
N. R. Mudigonda, R. M. Rangayyan, and J. E. Leo Desautels. Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging, pp. 1215–1227, 2001.
W.E. Polakowski, D.A. Cournoyer, S.K. Rogers, M.P. Desimio, D.W. Ruck, J.W. Hoffmeister, and R.A. Raines. Computer-aided breast-cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency. IEEE Trans. on Medical Imaging, pp. 811–819, 1997.
N. Karssemeijer and J. H. C. L. Hendriks. Computer-assisted reading of mammograms. European Radiology, pp. 743–748. 1997.
A. Rojas Domínguez and A. K. Nandi. Detection of masses in mammograms using enhanced multilevel-thresholding segmentation and region selection based on rank. In BIEN ’07: Proceedings of the fifth IASTED International Conference, ACTA Press, pp. 370–375. 2007.
C. A. Z. Barcelos, M. Boaventura, and E. C. Silva Jr. A well-balanced flow equation for noise removal and edge detection. IEEE Trans. on Image Processing, pp. 751–763, 2003.
J. Parker J. Suckling and D. R. Dance. The mammographic image analysis society digital mammogram database. International Congress Series, pp. 375–378. 1994.
D. Kopans R. Moore M. Heath, K. Bowyer and P. Kegelmeyer Jr. The digital database for screening mammography. Digital Mammography, pp. 212–218. 2000.
American College of Radiology. Breast Imaging Reporting and Data System BI-RADS. American College of Radiology, Reston, VA, 4th edition, 2004.
P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 629–639, 1990.
F. Catte, P. L. Lions, J. M. Morel, and T. Coll. Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal., pp. 182–193, 1992.
R. M. Rangayyan, F. J. Ayres, and J. E. Leo, Desautels. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of The Franklin Institute, pp. 312–348, 2007.
M. L. Giger. Computer-aided diagnosis of breast lesions in medical images. IEEE Computer Society Press, pp. 39–45, Los Alamitos, CA, USA, 1995.
Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, and C. E. Metz. Computadorized classification of benign and malignant masses on digitized mammograms: A study of robustness. Academic Radiology, pp. 1077–1084. 2000.
Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz. Breast cancer: effectiveness of computer-aided diagnosis - observer study with independent database of mammograms. Academic Radiology, pp. 224–256. 2002.
S. Gupta, Chyn P.F., and M.K. Markey. Breast cancer cadx based on bi-rads descriptors from two mammographic views. Med. Phys, pp. 1810–1817. 2006.
E. D. C. Anderson, B. Muir, J. S. Walsh, and A. E. Kirkpatrick. The efficacy of double reading mammograms in breast screening. Clinical Radiology, pp. 248–251. 1994.
E. L. Thurfjell, K. A. Lernevall, and A. Taube. Benefit of independent double reading in a population-based mammography screening program. Radiological Society, pp. 241–244. 1994.
B. Verma and J. Zakos. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans. on Information Technology in Biomedicine, pp. 46–54, 2001.
J. A. Baker, E. L.Rosen, and J. Y. Lo. Computer-aided detection (cad) in screening mammography: Sensitivity of commercial cad systems for detection architectural distortion. American Journal of Roentgenology, pp. 1083–1088, 2003.
S. M. Astley and F. J. Gilbert. Computer-aided detection in mammography. Clinical Radiology, pp. 390–399. 2004.
N. Petrick, H. P. Chan, and B. Sahiner. An adaptive density-weighted contrast enhancement filter for mamographic breast mass detection. IEEE Trans. on Medical Imaging, pp. 59–67, 1996.
N. R. Mudigonda, R. M. Rangayyan, and J. E. Leo Desautels. Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging, pp. 1215–1227, 2001.
W.E. Polakowski, D.A. Cournoyer, S.K. Rogers, M.P. Desimio, D.W. Ruck, J.W. Hoffmeister, and R.A. Raines. Computer-aided breast-cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency. IEEE Trans. on Medical Imaging, pp. 811–819, 1997.
N. Karssemeijer and J. H. C. L. Hendriks. Computer-assisted reading of mammograms. European Radiology, pp. 743–748. 1997.
A. Rojas Domínguez and A. K. Nandi. Detection of masses in mammograms using enhanced multilevel-thresholding segmentation and region selection based on rank. In BIEN ’07: Proceedings of the fifth IASTED International Conference, ACTA Press, pp. 370–375. 2007.
C. A. Z. Barcelos, M. Boaventura, and E. C. Silva Jr. A well-balanced flow equation for noise removal and edge detection. IEEE Trans. on Image Processing, pp. 751–763, 2003.
J. Parker J. Suckling and D. R. Dance. The mammographic image analysis society digital mammogram database. International Congress Series, pp. 375–378. 1994.
D. Kopans R. Moore M. Heath, K. Bowyer and P. Kegelmeyer Jr. The digital database for screening mammography. Digital Mammography, pp. 212–218. 2000.
American College of Radiology. Breast Imaging Reporting and Data System BI-RADS. American College of Radiology, Reston, VA, 4th edition, 2004.
P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 629–639, 1990.
F. Catte, P. L. Lions, J. M. Morel, and T. Coll. Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal., pp. 182–193, 1992.
Publicado
12/07/2008
Como Citar
GULIATO, Denise; BARCELOS, Celia A. Zorzo; DIAS, Walter B..
O uso de Equações de Difusão no Processo de Detecção de Regiões Suspeitas em Mamografias. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 8. , 2008, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2008
.
p. 121-130.
ISSN 2763-8952.
