Classification of Breast Tumors Through Image Mining Techniques
Resumo
In this paper a methodology and a computer-aided diagnosis system for detection of breast cancer are proposed. The approach involves Image Processing resources to extract morphological features from tumors in mammograms and Image Mining to classify them as benign or malignant. Images from BCDR repository were used for the experiments. The results showed the efficacy of the proposed method and system, which reduced the false positive and false negative rates, and allowed a more efficient decision-making process.
Referências
ACR (1993). American college of radiology breast imaging reporting and data system (bi-rads). Reston, VA: American College of Radiology.
Al-Shamlan, H. and El-Zaart, A. (2010). Feature extraction values for breast cancer mammography images. International Conference on Bioinformatics and Biomedical Technology, pages 335–340.
Alvarenga, A. V., Infantosi, A. F. C., de Azevedo, C. M., and de Albuquerque Pereira, W. C. (2003). Application of morphological operators in the segmentation and determination of the contour of breast tumors in ultrasound images. Brazilian Journal of Biomedical Engineering, 19(2):91–101.
Asad, M., Azeemi, N. Z., Zafar, M. F., and Naqvi, S. (2011). Early stage breast cancer detection through mammographic feature analysis. Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on. IEEE, pages 1–4.
BCDR (2018). Breast cancer digital repository. [link].
Calas, M. J. G., Gutfilen, B., and de A. Pereira, W. C. (2012). Cad and mammography: why use this tool? Brazilian Board of Radiology and Image Diagnosis, 1:46–52.
Costa, A. F., Humpire-Mamani, G., and Traina, A. J. M. (2012). An efficient algorithm for fractal analysis of textures. SIBGRAPI, 25th Brazilian Symposium on Computer Graphics and Image Processing:39–46.
Cuadros, O., Botelho, G., Rodrigues, F., and Neto, J. B. (2012). Segmentation of large images with complex networks. SIBGRAPI, 25th Brazilian Symposium on Computer Graphics and Image Processing:24–31.
dos Santos Romualdo, L. C., da Costa Vieira, M. A., and Schiabel, H. (2009). Mammography images restoration by quantum noise reduction and inverse mtf filtering. SIBGRAPI, 22th Brazilian Symposium on Computer Graphics and Image Processing:180–185.
Ferreira, C. B. R. and Borges, D. L. (2005). A selection strategy of a minimal subset of wavelet features in a multiresolution approach for the classification of tumors on mammograms. V Workshop of Medical Informatics, 1.
Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing. Prentice Hall, 5 edition.
Leite, G. C., Leite, J. S. S., Meneses, F. G. A., Santos, D. A., and Silva, J. S. (2011). The use of thresholding techniques to aid in the diagnosis of breast cancer. III National Encounter Of Biomechanical Engineering.
Liu, X. and Tang, J. (2014). Mass classification in mammograms using selected geometry and texture features, and a new svm-based feature selection method. IEEE Systems Journal, 8(3):910–920.
Nixon, M. S. and Aguado, A. S. (2008). Feature Extraction & Image Processing. Academic Press, 2 edition.
Radovic, M., Djokovic, M., Peulic, A., and Filipovic, N. (2013). Application of data mining algorithms for mammogram classification. Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE, pages 1–4.
Skaane, P., Engedal, K., and Skjennald, A. (1997). Interobserver variation in the interpretation of breast imaging. Acta Radiol, pages 497–502.
Solomon, C. and Breckon, T. (2011). Fundamentals of digital image processing: a practical approach with examples in matlab. Wiley-Blackwell.
Souto, L. P. M., dos Santos, T. K. L., and dos Santos Silva, M. P. (2014). Métricas morfológicas para a classificação de tumores de mama. Workshop de Informática Médica. CSBC.
Surendiran, B. and Vadivel, A. (2012). Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer. Int. J. Medical Engineering and Informatics, 4(1):36–54.
Thurfjell, E., Lernevall, K., and Taube, A. (1994). Benefit of independent double reading in a populationbased mammography screening program. Radiology, pages 241–244.
Al-Shamlan, H. and El-Zaart, A. (2010). Feature extraction values for breast cancer mammography images. International Conference on Bioinformatics and Biomedical Technology, pages 335–340.
Alvarenga, A. V., Infantosi, A. F. C., de Azevedo, C. M., and de Albuquerque Pereira, W. C. (2003). Application of morphological operators in the segmentation and determination of the contour of breast tumors in ultrasound images. Brazilian Journal of Biomedical Engineering, 19(2):91–101.
Asad, M., Azeemi, N. Z., Zafar, M. F., and Naqvi, S. (2011). Early stage breast cancer detection through mammographic feature analysis. Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on. IEEE, pages 1–4.
BCDR (2018). Breast cancer digital repository. [link].
Calas, M. J. G., Gutfilen, B., and de A. Pereira, W. C. (2012). Cad and mammography: why use this tool? Brazilian Board of Radiology and Image Diagnosis, 1:46–52.
Costa, A. F., Humpire-Mamani, G., and Traina, A. J. M. (2012). An efficient algorithm for fractal analysis of textures. SIBGRAPI, 25th Brazilian Symposium on Computer Graphics and Image Processing:39–46.
Cuadros, O., Botelho, G., Rodrigues, F., and Neto, J. B. (2012). Segmentation of large images with complex networks. SIBGRAPI, 25th Brazilian Symposium on Computer Graphics and Image Processing:24–31.
dos Santos Romualdo, L. C., da Costa Vieira, M. A., and Schiabel, H. (2009). Mammography images restoration by quantum noise reduction and inverse mtf filtering. SIBGRAPI, 22th Brazilian Symposium on Computer Graphics and Image Processing:180–185.
Ferreira, C. B. R. and Borges, D. L. (2005). A selection strategy of a minimal subset of wavelet features in a multiresolution approach for the classification of tumors on mammograms. V Workshop of Medical Informatics, 1.
Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing. Prentice Hall, 5 edition.
Leite, G. C., Leite, J. S. S., Meneses, F. G. A., Santos, D. A., and Silva, J. S. (2011). The use of thresholding techniques to aid in the diagnosis of breast cancer. III National Encounter Of Biomechanical Engineering.
Liu, X. and Tang, J. (2014). Mass classification in mammograms using selected geometry and texture features, and a new svm-based feature selection method. IEEE Systems Journal, 8(3):910–920.
Nixon, M. S. and Aguado, A. S. (2008). Feature Extraction & Image Processing. Academic Press, 2 edition.
Radovic, M., Djokovic, M., Peulic, A., and Filipovic, N. (2013). Application of data mining algorithms for mammogram classification. Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE, pages 1–4.
Skaane, P., Engedal, K., and Skjennald, A. (1997). Interobserver variation in the interpretation of breast imaging. Acta Radiol, pages 497–502.
Solomon, C. and Breckon, T. (2011). Fundamentals of digital image processing: a practical approach with examples in matlab. Wiley-Blackwell.
Souto, L. P. M., dos Santos, T. K. L., and dos Santos Silva, M. P. (2014). Métricas morfológicas para a classificação de tumores de mama. Workshop de Informática Médica. CSBC.
Surendiran, B. and Vadivel, A. (2012). Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer. Int. J. Medical Engineering and Informatics, 4(1):36–54.
Thurfjell, E., Lernevall, K., and Taube, A. (1994). Benefit of independent double reading in a populationbased mammography screening program. Radiology, pages 241–244.
Publicado
22/07/2018
Como Citar
SOUTO, Lizianne P. Marques; DOS SANTOS, Thiago K. L.; SILVA, Marcelino Pereira S..
Classification of Breast Tumors Through Image Mining Techniques. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1-11.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3667.
