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Mediterranean Conference on Medical and Biological Engineering and Computing

MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 pp 322–332Cite as

Calculating Texture Features from Mammograms and Evaluating Their Performance in Classifying Clusters of Microcalcifications

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Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

Abstract

In this work, 2432 texture features were calculated from microcalcification clusters presented on 190 images from the Digital Database for Screening Mammography. Mutual information technique was used to rank texture features. Then, an incremental procedure adds top ranked features to the Fisher discriminant analysis to determine the best set of texture features in classifying benign or malignant microcalcification clusters. The result was achieved using 13 texture features (AUC.632+ = 0.945 ± 0.019). However, to assure a consistent statistical analysis, at least 30 sample images for each feature added was assumed. The best performance was achieved by a set with 5 texture features (AUC.632+ = 0.884 ± 0.025), which is comparable to the ones presented in literature.

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References

  1. Brasil, Ministério da Saúde, Instituto Nacional de Câncer (INCA), Tipos de Câncer, Câncer de Mama. http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/mama

  2. Brasil, Ministério da Saúde, Instituto Nacional de Câncer (INCA), Câncer de Mama, Detecção Precoce. http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/mama/deteccao_precoce+

  3. Jalalian, A., Mashoror, S.B.T., Mahmudb, H.R., Saripan, M.I.B., Ramli, A.R.B., Karasfi, B.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013)

    Article  Google Scholar 

  4. Calas, M.J.G., Gutfilen, B., Pereira, W.C.A.: CAD and mammography: why use this tool? Braz. J. Biomed. Eng. 45(1), 46–52 (2012)

    Google Scholar 

  5. Wei, L., Yanga, Y., Nishikawa, R.M.: Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recogn. 42(6), 1126–1132 (2009)

    Article  Google Scholar 

  6. Halkiots, S., Botsis, T., Rangoussi, M.: Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Sig. Process. 87(7), 1559–1568 (2007)

    Article  MATH  Google Scholar 

  7. Nishikawa, R.M.: Current status and future directions of computer-aided diagnosis in mammography. Comput. Med. Imaging Graph. 31(4–5), 224–235 (2007)

    Article  Google Scholar 

  8. Elter, M., Horsch, A.: CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 36(6), 2052–2068 (2009)

    Article  Google Scholar 

  9. Chen, C.H., Lee, G.G.: On digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis. Graph. Models Image Process. 59(5), 349–364 (1997)

    Article  Google Scholar 

  10. Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 36(12), 2967–2991 (2003)

    Article  MATH  Google Scholar 

  11. Arikidis, N.S., Karajaliou, A., Skiadopoulos, S., Korfiatis, P., Likaki, E., Panayiotakis, G., et al.: Size-adapted microcalcification segmentation in mammography utilizing scale-space signatures. Comput. Med. Imaging Graph. 34(6), 487–493 (2010)

    Article  Google Scholar 

  12. Paquerault, S., Yarusso, L.M., Papaioannou, J., Jiang, Y.: Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med. Phys. 31(9), 2648–2657 (2004)

    Article  Google Scholar 

  13. De Santo, M., Molinara, M., Tortorella, F., Vento, M.: Automated classification of clustered microcalcifications by a multiple expert system. Pattern Recogn. 36(7), 1467–1477 (2003)

    Article  Google Scholar 

  14. Veldkamp, W.J., Karssemeijer, N., Otten, J.D.M., Hendriks, J.H.C.L.: Automated classification of clusters microcalcifications into malignant and benign types. Med. Phys. 27(11), 2600–2608 (2000)

    Article  Google Scholar 

  15. Chan, H.P., Sahiner, B.: Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Med. Phys. 25(10), 2007–2019 (1998)

    Article  Google Scholar 

  16. Karahaliou, A.N., Boniatis, I.S., Skiadopoulos, S.G., Sakellaropoulos, F.N., Likaki, E., Panayiotakis, G.S., Costaridou, L.I.: A texture analysis approach for characterizing microcalcifications on mammograms. In: Proceeding of IEEE International Special Topic Conference on Information Technology in Biomedicine (ITAB 2006), pp. 251–257, October 2006

    Google Scholar 

  17. Karahaliou, A.N., Boniatis, I.S., Skiadopoulos, S.G., Sakellaropoulos, F.N., Arikidis, N.S., Likaki, E.A., Panayiotakis, G.S., Costaridou, L.I.: Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf Technol. Biomed. 12(6), 731–738 (2008)

    Article  Google Scholar 

  18. Flores, W.G., Pereira, W.C.A., Infantosi, A.F.C.: Improving classification performance of breast lesions on ultrasonography. Pattern Recogn. 48(4), 1125–1136 (2015)

    Article  Google Scholar 

  19. University of South Florida, DOD Breast Cancer Research Program, US Army Research and Material Command, Digital Database for Screening Mammography (DDSM). http://marathon.csee.usf.edu/Mammography/Database.html. Accessed 03 Jan 2014

  20. Zhang, P., Verma, B., Kumar, K.: Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recogn. Lett. 26(7), 909–919 (2005)

    Article  Google Scholar 

  21. Alvarenga, A.V., Pereira, W.C.A., Infantosi, A.F.C., Azevedo, C.M.: Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images. Med. Phys. 34(2), 379–387 (2007)

    Article  Google Scholar 

  22. Gomez, W., Pereira, W., Infantosi, A.F.C.: Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans. Med. Imaging 31(10), 1889–1899 (2012)

    Article  Google Scholar 

  23. Kriti, J.V., Dey, N., Kumar, V.: PCA-PNN and PCA-SVM based CAD systems for breast density classification. In: Applications of Intelligent Optimization in Biology and Medicine: Current Trends and Open Problems, pp. 159–180 (2016)

    Google Scholar 

  24. Chang, R.-F., Wu, W.-J., Moon, W.K., Chen, D.-R.: Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med. Biol. 29(5), 679–686 (2003)

    Article  Google Scholar 

  25. Shen, W.-C., Chang, R.-F., Moon, W.K., Chou, Y.-H., Huang, C.-S.: Breast ultrasound computer-aided diagnosis using bi-rads features. Acad. Radiol. 14(8), 928–939 (2007)

    Article  Google Scholar 

  26. Huang, Y.-L., Wang, K.-L., Chen, D.-R.: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput. Appl. 15(2), 164–169 (2006)

    Article  Google Scholar 

  27. Fernandez, A., Alvarez, M.X., Bianconi, F.: Image classification with binary gradient contours. Opt. Lasers Eng. 49, 1177–1184 (2011)

    Article  Google Scholar 

  28. Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.J.: Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29(2), 157–164 (2002)

    Article  Google Scholar 

  29. Duarte, M.A., Alvarenga, A.V., Azevedo, C.M., Calas, M.J.G., Infantosi, A.F.C., Pereira, W.C.A.: Segmenting mammographic microcalcifications using a semi-automatic procedure based on Otsu’s method morphological filters. Braz. J. Biomed. Eng. 29(4), 377–388 (2013)

    Google Scholar 

  30. Duarte, M.A., Alvarenga, A.V., Azevedo, C.M., Calas, M.J.G., Infantosi, A.F.C., Pereira, W.C.A.: Evaluating geodesic active contours in microcalcifications segmentation on mammograms. Comput. Methods Prog. Biomed. 122(3), 304–315 (2015)

    Article  Google Scholar 

  31. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  32. Pereira, W.C., Alvarenga, A.V., Infantosi, A.F.C., Macrini, L., Pedreira, C.E.: A non-linear morphometric feature selection approach for breast tumor contour from ultrasonic images. Comput. Biol. Med. 11(12), 912–918 (2010)

    Article  Google Scholar 

  33. Sahiner, B., Chan, H.P., Hadjiiski, L.: Classifier performance prediction for computer-aided diagnosis using a limited dataset. Med. Phys. 35(4), 1559–1570 (2008)

    Article  Google Scholar 

  34. Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027–1061 (2007)

    MATH  Google Scholar 

  35. Stoline, M.R.: The status of multiple comparisons: simultaneous estimation of all pairwise comparisons in one-way ANOVA designs. Am. Stat. 35(3), 134–141 (1981)

    MATH  Google Scholar 

  36. Dheeba, J., Tamil Selvi, S.: Classification of malignant and benign microcalcification using SVM classifier. In: Proceedings of the 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT 2011), India, pp. 686–690, March 2011

    Google Scholar 

  37. Tiedeu, A., Daul, C., Kentsop, A., Graebling, P., Wolf, D.: Texture-based analysis of clustered microcalcifications detected on mammograms. Digital Sig. Process. 22(1), 124–132 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

Thanks to the financial support of the Brazilian National Council for Scientific and Technological Development (CNPq) (Grants: 434.858/2016-1, 309717/2014-0, and 308.627/2013-0), and CAPES/PROEX.

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Authors and Affiliations

  1. Electronic Engineering Department, UniCarioca University Centre, Rio de Janeiro, Brazil

    Marcelo A. Duarte

  2. Biomedical Engineering Program/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Wagner C. A. Pereira

  3. Laboratory of Ultrasound, National Institute of Metrology, Quality and Technology, Rio de Janeiro, Brazil

    André Victor Alvarenga

Authors
  1. Marcelo A. Duarte
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  2. Wagner C. A. Pereira
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  3. André Victor Alvarenga
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Correspondence to André Victor Alvarenga .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Henriques

  2. Universidade do Minho, Braga, Portugal

    Nuno Neves

  3. University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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Duarte, M.A., Pereira, W.C.A., Alvarenga, A.V. (2020). Calculating Texture Features from Mammograms and Evaluating Their Performance in Classifying Clusters of Microcalcifications. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_39

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