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Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

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Abstract

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented (\(p<0.05\)). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

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References

  1. American Cancer Society: Breast Cancer Facts& Figures 2005–2006. American Cancer Society, Atlanta (2006)

    Google Scholar 

  2. Kuhl, C.K., Mielcareck, P., Klaschik, S., Leutner, C., Wardelmann, E., Gieseke, J., Schild, H.H.: Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211(1), 101–110 (1999)

    Article  Google Scholar 

  3. Wedegärtner, U., Bick, U., Wörtler, K., Rummeny, E., Bongartz, G.: Differentiation between benign and malignant findings on MR-mammography: usefulness of morphological criteria. Eur. Radiol. 11(9), 1645–1650 (2001)

    Article  Google Scholar 

  4. Baum, F., Fischer, U., Vosshenrich, R., Grabbe, E.: Classification of hypervascularized lesions in CE MR imaging of the breast. Eur. Radiol. 12(5), 1087–1092 (2002)

    Article  Google Scholar 

  5. Szabo, B., Aspelin, P., Wiberg, M., Böne, B.: Dynamic MR imaging of the breast: analysis of kinetic and morphologic diagnostic criteria. Acta Radiol. 44(4), 379–386 (2003)

    Google Scholar 

  6. Chen, W., Giger, M.L., Lan, L., Bick, U.: Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med. Phys. 31(5), 1076–1082 (2004)

    Article  Google Scholar 

  7. Wismüller, A., Meyer-Base, A., Lange, O., Schlossbauer, T., Kallergi, M., Reiser, M.F., Leinsinger, G.: Segmentation and classification of dynamic breast magnetic resonance image data. J. Electr. Imaging 15(1) (2006)

  8. Gibbs, P., Turnbull, L.W.: Textural analysis of contrast-enhanced MR images of the breast. Magn. Reson. Med. 50(1), 92–98 (2003)

    Article  Google Scholar 

  9. Chen, W., Giger, M.L., Li, H., Bick, U., Newstead, G.M.: Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn. Reson. Med. 58(3), 562–571 (2007)

    Article  Google Scholar 

  10. Nie, K., Chen, J.H., Yu, H.J., Chu, Y., Nalcioglu, O., Su, M.Y.: Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad. Radiol. 15(12), 1513–1525 (2008)

    Article  Google Scholar 

  11. Meinel, L.A., Stolpen, A.H., Berbaum, K.S., Fajardo, L.L., Reinhardt, J.M.: Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J. Magn. Reson. Imaging 25(1), 89–95 (2007)

    Article  Google Scholar 

  12. Fischer, D., Wurdinger, S., Boettcher, J., Malich, A., Kaiser, W.: Further signs in the evaluation of magnetic resonance mammography: a retrospective study. Investig. Radiol. 40(7), 430–435 (2005)

    Article  Google Scholar 

  13. Schlossbauer, T., Leinsinger, G., Wismüller, A., Lange, O., Scherr, M., Meyer-Baese, A., Reiser, M.: Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization. Investig. Radiol. 43(1), 56–64 (2008)

    Article  Google Scholar 

  14. Leinsinger, G., Schlossbauer, T., Scherr, M., Lange, O., Reiser, M., Wismüller, A.: Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions? Eur. Radiol. 16(5), 1138–1146 (2006)

    Article  Google Scholar 

  15. Gilhuijs, K.G.A., Giger, M., Bick, U.: Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med. Phys. 25(9), 1647–1654 (1998)

    Article  Google Scholar 

  16. Zheng, Y., Englander, S., Baloch, S., Zacharaki, E.I., Fan, Y., Schnall, M.D., Shen, D.: STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis. Med. Phys. 36(7), 3192–3204 (2009)

    Article  Google Scholar 

  17. Buelow, T., Saalbach, A., Bergtholdt, M., Wiemker, R., Buurman, H., Meinel, L.A., Newstead, G.: Heterogeneity of kinetic curve parameters as indicator for the malignancy of breast lesions in DCE MRI. Proc. SPIE Med. Imaging 7624 (2010)

  18. Agner, S., Soman, S., Libfield, E., McDonald, M., Thomas, K., Englander, S., Rosen, M., Chin, D., Nosher, J., Madabhushi, A.: Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J. Digital Imaging 24(3), 446–463 (2011)

    Article  Google Scholar 

  19. Nagarajan, M.B., Huber, M.B., Schlossbauer, T., Leinsinger, G., Krol, A., Wismüller, A.: Classification of Small Lesions on Breast MRI: evaluating the role of dynamically extracted texture through feature selection. J. Med. Biol. Eng.(2012). doi:10.5405/jmbe.1183

  20. Ikeda, D.M., Hylton, N.M., Kinkel, K., Hochman, M.G., Kuhl, C.K., Kaiser, W.A., Weinreb, J.C., Smazal, S.F., Degani, H., Viehweg, P., Barclay, J., Schnall, M.D.: Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging studies. J. Magn. Reson. Imaging 13(6), 889–895 (2001)

    Article  Google Scholar 

  21. D’Orsi, C.J., Bassett, L., Berg, W., Feig, S., Jackson, J., Kopans, D.: Breast Imaging Reporting and Data System (BI-RADS) Breast Imaging Atlas. American College of Radiology, Reston (2003)

    Google Scholar 

  22. Michielsen, K., Raedt, H.: Integral-geometry morphological image analysis. Phys. Rep. 347(6), 461–538 (2001)

    Google Scholar 

  23. Boehm, H., Fink, C., Attenberger, U., Becker, C., Behr, J., Reiser, M.: Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D. Eur. Radiol. 18(12), 2745–2755 (2008)

    Article  Google Scholar 

  24. Huber, M., Nagarajan, M., Leinsinger, G., Eibel, R., Ray, L., Wismüller, A.: Performance of topological texture features to classify fibrotic interstitial lung disease patterns. Med. Phys. 38(4), 2035–2044 (2011)

    Article  Google Scholar 

  25. Boehm, H., Vogel, T., Panteleon, A., Burklein, D., Bitterling, H., Reiser, M.: Differentiation between post-menopausal women with and without hip fractures: enhanced evaluation of clinical DXA by topological analysis of the mineral distribution in the scan images. Osteoporos Int. 18(6), 779–787 (2007)

    Article  Google Scholar 

  26. Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)

    Article  Google Scholar 

  27. Drucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. Adv. Neural Inf. Process. Syst. 9, 155–161 (1996)

    Google Scholar 

  28. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  29. Chen, W.J., Giger, M.L., Bick, U.: A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad. Radiol. 13(1), 63–72 (2006)

    Article  Google Scholar 

  30. Haralick, R.M., Shanmuga, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. Smc3(6), 610–621 (1973)

    Google Scholar 

  31. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  32. Tourassi, G., Frederick, E., Markey, M., Floyd Jr, C.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med. Phys. 28(12), 2394–2402 (2001)

    Article  Google Scholar 

  33. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2000)

    Google Scholar 

  34. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm. ACM Trans. Intell. Syst. Technol. 2 27(1–27), 27 (2011)

    Google Scholar 

  35. Wright, S.P.: Adjusted P-values for simultaneous inference. Biometrics 48(4), 1005–1013 (1992)

    Article  Google Scholar 

  36. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)

    MathSciNet  MATH  Google Scholar 

  37. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

    Article  Google Scholar 

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Acknowledgments

The authors would also like to thank Benjamin Mintz for his assistance with developing the tool used for lesion annotation, and Prof. Dr. Maximilian Reiser, FACR, FRCR of the Department of Radiology, Ludwig Maximilians University, for his continued support.

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Correspondence to Mahesh B. Nagarajan.

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This research was funded in part by the National Institute of Health (NIH) Award R01-DA-034977, the Clinical and Translational Science Award 5-28527 within the Upstate New York Translational Research Network (UNYTRN) of the Clinical and Translational Science Institute (CTSI), University of Rochester, and by the Center for Emerging and Innovative Sciences (CEIS), a NYSTAR-designated Center for Advanced Technology. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

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Nagarajan, M.B., Huber, M.B., Schlossbauer, T. et al. Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement . Machine Vision and Applications 24, 1371–1381 (2013). https://doi.org/10.1007/s00138-012-0456-y

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