Dimensão Fractal: Quantificação dos Comportamentos de Nódulos Mamários Malignos e Estruturas Adjacentes
Resumo
Neste trabalho é apresentado um método para quantificar imagem mamográfica com e sem nódulos mamários malignos, além das estruturas adjacentes aos nódulos. Regiões de interesse de cada imagem foram segmentadas com um método semi-automático e multinível, que determina o limiar a partir de análises dos percentuais de inclinação e comparações das entropias em diferentes regiões do histograma da imagem em questão. A quantificação das estruturas foi com a dimensão fractal multiescala, para identificar padrões de comportamentos das estruturas de interesse, fornecendo informações relevantes para embasar Sistemas de Apoio ao Diagnóstico (SAD).Referências
Nishikawa, R. (2007) Current status and future directions of computer-aided diagnosis in mammography, In Computerized Medical Imaging and Graphics, v. 31, page 224–235
INCA - Ministério da Saúde. Secretaria de atenção à saúde. Inst. Nac. do Câncer. Coord. de prev. e vigilância; (2009). Estimativa 2010. Incidência de câncer no Brasil [link], 19/05/2011.
Gupta, S., Chyn, P. F., Markey, M. K. (2006) Breast cancer CADx based on BI-RADS descriptors from two mammographic views. In Medical Physics, pages 1810-1817.
Wei, J., Chan, H., Zhou, C., Wu, Y., Sahiner, B., Hadjiiski, L. M., et al. (2011). Computer-aided detection of breast masses: Four-view strategy for screening mammography. In Medical Physics, v. 38(4),pages 1867-1876.
Rangayyan, R., Nguyen, T., (2005) “Pattern classification of breast masses via fractal analysis of their contours”, In: Proceedings of Computer Assisted radiology and Surgery (CARS), pages 1041–1046.
Mavroforakis, M., Georgiou, H., Dimitropoulos, N., Cavouras, D., Theodoridis, S. (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. In Artificial Intelligence in Medicine, pages 145–162.
Stojic, T., Reljin, I., Reljin, B. (2006) Adaptation of multifractal analysis to segmentation of microcalcications in digital mammograms. In Physica A Statistical Mechanics and its Applications, v. 367, pages 494–508.
Pruess, S. (2007) “Some remarks on the numerical estimation of fractal dimension”, Fractals in the Earth Sciences, C.C. Barton and P.R. La Pointe, New-York, Plenum Press, Chap. 3, pages 65–75.
Li, H., Giger, M.L., Olopade, O.I., Lan, L. (2007) Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. In Academic Radiology, v.14 (5), pages 513–521.
Dua, S., Singh, H., Thompson, H. W. (2010) Associative Classification of Mammograms using Weighted Rules. In Expert Syst Appl. v. 36 (5), pages 9250-9259.
Backes, A. R. and Bruno, O. M. (2006) Segmentação de texturas por análise de complexidade. In INFOCOMP Journal of Computer Science, v. 5 (1), pages 87–95.
Chaudhuri, B. B. and Sarkar, N. (1995) Texture Segmentation Using Fractal Dimension. In IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17, pages 72-76.
Coelho, R. C. and Costa, L. da F. (1995) “The Box-Counting Fractal Dimension: Does it provide an Accurate Subsidy for Experimental Shape Characterization? If So, How to Use It?” In: Symp. on Comp. Graphics and Image Processing, São Carlos, Brazil, page. 183-191.
Plotnick, R. E, Gradner, R. H., Hargrove, W. W., Prestegaard, K. and Perlmutter, M. (1996) Lacunarity analysis: a general technique for the analysis of spatial patterns. In Physical Review E, v. 53, pages 5461-5468.
Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, F. (2008) Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. In IEEE Trans. on Instrumentation and Measurement, pages 1422-1430.
Neves, L. A., Oliveira, F. R., Peres, F. A., Moreira, R. D., Moriel, A. R., de Godoy, M. F. and Murta Junior, L. O. (2011) Maximum entropy, fractal dimension and lacunarity in quantification of cellular rejection in myocardial biopsy of patients submitted to heart transplantation. In: Journal of Physics: Conference Series, v. 285 (1), pages 012032.
Pun, T., (1980) A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. In Signal Processing, v. 2, pages 223-237.
Fawcett, T. (2006) An introduction to ROC Analysis. In Pattern Recognition Letters, v. 27, pages 861-874.
INCA - Ministério da Saúde. Secretaria de atenção à saúde. Inst. Nac. do Câncer. Coord. de prev. e vigilância; (2009). Estimativa 2010. Incidência de câncer no Brasil [link], 19/05/2011.
Gupta, S., Chyn, P. F., Markey, M. K. (2006) Breast cancer CADx based on BI-RADS descriptors from two mammographic views. In Medical Physics, pages 1810-1817.
Wei, J., Chan, H., Zhou, C., Wu, Y., Sahiner, B., Hadjiiski, L. M., et al. (2011). Computer-aided detection of breast masses: Four-view strategy for screening mammography. In Medical Physics, v. 38(4),pages 1867-1876.
Rangayyan, R., Nguyen, T., (2005) “Pattern classification of breast masses via fractal analysis of their contours”, In: Proceedings of Computer Assisted radiology and Surgery (CARS), pages 1041–1046.
Mavroforakis, M., Georgiou, H., Dimitropoulos, N., Cavouras, D., Theodoridis, S. (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. In Artificial Intelligence in Medicine, pages 145–162.
Stojic, T., Reljin, I., Reljin, B. (2006) Adaptation of multifractal analysis to segmentation of microcalcications in digital mammograms. In Physica A Statistical Mechanics and its Applications, v. 367, pages 494–508.
Pruess, S. (2007) “Some remarks on the numerical estimation of fractal dimension”, Fractals in the Earth Sciences, C.C. Barton and P.R. La Pointe, New-York, Plenum Press, Chap. 3, pages 65–75.
Li, H., Giger, M.L., Olopade, O.I., Lan, L. (2007) Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. In Academic Radiology, v.14 (5), pages 513–521.
Dua, S., Singh, H., Thompson, H. W. (2010) Associative Classification of Mammograms using Weighted Rules. In Expert Syst Appl. v. 36 (5), pages 9250-9259.
Backes, A. R. and Bruno, O. M. (2006) Segmentação de texturas por análise de complexidade. In INFOCOMP Journal of Computer Science, v. 5 (1), pages 87–95.
Chaudhuri, B. B. and Sarkar, N. (1995) Texture Segmentation Using Fractal Dimension. In IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17, pages 72-76.
Coelho, R. C. and Costa, L. da F. (1995) “The Box-Counting Fractal Dimension: Does it provide an Accurate Subsidy for Experimental Shape Characterization? If So, How to Use It?” In: Symp. on Comp. Graphics and Image Processing, São Carlos, Brazil, page. 183-191.
Plotnick, R. E, Gradner, R. H., Hargrove, W. W., Prestegaard, K. and Perlmutter, M. (1996) Lacunarity analysis: a general technique for the analysis of spatial patterns. In Physical Review E, v. 53, pages 5461-5468.
Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, F. (2008) Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. In IEEE Trans. on Instrumentation and Measurement, pages 1422-1430.
Neves, L. A., Oliveira, F. R., Peres, F. A., Moreira, R. D., Moriel, A. R., de Godoy, M. F. and Murta Junior, L. O. (2011) Maximum entropy, fractal dimension and lacunarity in quantification of cellular rejection in myocardial biopsy of patients submitted to heart transplantation. In: Journal of Physics: Conference Series, v. 285 (1), pages 012032.
Pun, T., (1980) A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. In Signal Processing, v. 2, pages 223-237.
Fawcett, T. (2006) An introduction to ROC Analysis. In Pattern Recognition Letters, v. 27, pages 861-874.
Publicado
19/07/2011
Como Citar
NEVES, Leandro A.; NASCIMENTO, Marcelo Z. do; GODOY, Moacir F. de.
Dimensão Fractal: Quantificação dos Comportamentos de Nódulos Mamários Malignos e Estruturas Adjacentes. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 11. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1722-1731.
ISSN 2763-8952.
