Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework

Abstract

Ovarian cancer is one of the prime causes of mortality in women. Diagnosis of ovarian cancer using ultrasonography is tedious as ovarian tumors exhibit minute clinical and structural differences between the suspicious and non-suspicious classes. Early prediction of ovarian cancer will reduce its growth rate and may save many lives. Computer-aided diagnosis (CAD) is a noninvasive method for finding ovarian cancer in its early stage which can avoid patient anxiety and unnecessary biopsy. This study investigates the efficacy of a novel CAD tool to characterize suspicious ovarian cancer using Radon-transformed nonlinear features. The obtained dimension of the extracted features is reduced using Relief-F feature selection method. In this study, we have employed the fuzzy forest-based ensemble classifier in contrast to the known crisp rule-based classifiers. The proposed method is evaluated using 469 (non-suspicious: 238, suspicious: 231) subjects and achieved a maximum 80.60 ± 0.5% accuracy, 81.40% sensitivity, 76.30% specificity with fuzzy forest, an ensemble fuzzy classifier using thirty-nine features. The proposed method is robust and reproducible as it uses maximum number subjects (469) as compared to state-of-the-art techniques. Hence, it can be used as an assisting tool by gynecologists during their routine screening.

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References

  1. 1.

    NIH, “Ovarian, fallopian tube, and primary peritoneal cancer prevention (PDQ®)–patient version.” Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK65937/ (2017)

  2. 2.

    Acharya, U.R., Molinari, F., Sree, S.V., Swapna, G., Saba, L., Guerriero, S., et al.: Ovarian tissue characterization in ultrasound: a review. Technol. Cancer Res. Treat. 14, 251–261 (2015)

    Article  Google Scholar 

  3. 3.

    Acharya, U.R., Sree, S.V., Kulshreshtha, S., Molinari, F., Koh, J.E.W., Saba, L., et al.: GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization. Technol. Cancer Res. Treat. 13, 529–539 (2014)

    Article  Google Scholar 

  4. 4.

    Tan, T.Z., Quek, C., Ng, G.S., Razvi, K.: Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif. Intell. Med. 43, 207–222 (2008)

    Article  Google Scholar 

  5. 5.

    Tang, K.-L., Li, T.-H., Xiong, W.-W., Chen, K.: Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinform. 11, 109 (2010)

    Article  Google Scholar 

  6. 6.

    Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., et al.: Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359, 572–577 (2002)

    Article  Google Scholar 

  7. 7.

    Assareh, A., Moradi, M.H.: Extracting efficient fuzzy if-then rules from mass spectra of blood samples to early diagnosis of ovarian cancer. In: IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 2007. CIBCB’07. pp. 502–506. (2007)

  8. 8.

    Meng, H., Hong, W., Song, J., Wang, L.: Feature extraction and analysis of ovarian cancer proteomic mass spectra. In: The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. pp. 668–671. (2008)

  9. 9.

    Biagiotti, R., Desii, C., Vanzi, E., Gacci, G.: Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US. Radiology 210, 399–403 (1999)

    Article  Google Scholar 

  10. 10.

    Tailor, A., Jurkovic, D., Bourne, T., Collins, W., Campbell, S.: Sonographic prediction of malignancy in adnexal masses using multivariate logistic regression analysis. Ultrasound Obstet. Gynecol. 10, 41–47 (1997)

    Article  Google Scholar 

  11. 11.

    Lucidarme, O., Akakpo, J.-P., Granberg, S., Sideri, M., Levavi, H., Schneider, A., et al.: A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study. Eur. Radiol. 20, 1822–1830 (2010)

    Article  Google Scholar 

  12. 12.

    Zimmer, Y., Tepper, R., Akselrod, S.: An automatic approach for morphological analysis and malignancy evaluation of ovarian masses using B-scans. Ultrasound Med. Biol. 29, 1561–1570 (2003)

    Article  Google Scholar 

  13. 13.

    Acharya, U.R., Krishnan, M.M.R., Saba, L., Molinari, F., Guerriero, S., Suri, J.S.: Ovarian tumor characterization using 3D ultrasound. In: Saba, L., Acharya, U., Guerriero, S., Suri, J. (eds.) Ovarian Neoplasm Imaging, pp. 399–412. Springer, Boston, MA (2013)

    Google Scholar 

  14. 14.

    Acharya, U.R., Mookiah, M.R.K., Sree, S.V., Yanti, R., Martis, R., Saba, L., et al.: Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification. Ultraschall Medizin Eur. J. Ultrasound 35, 237–245 (2014)

    Google Scholar 

  15. 15.

    Acharya, U.R., Sree, S.V., Saba, L., Molinari, F., Guerriero, S., Suri, J.S.: Ovarian tumor characterization and classification using ultrasound—a new online paradigm. J. Digit. Imaging 26, 544–553 (2013)

    Article  Google Scholar 

  16. 16.

    Hata, T., Yanagihara, T., Hayashi, K., Yamashiro, C., Ohnishi, Y., Akiyama, M., et al.: Three-dimensional ultrasonographic evaluation of ovarian tumours: a preliminary study. Hum. Reprod. 14, 858–862 (1999)

    Article  Google Scholar 

  17. 17.

    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)

    Article  Google Scholar 

  18. 18.

    Wang, X., Wong, B.S., Guan, T.C.: Image enhancement for radiography inspection. In: Proceedings of SPIE, pp. 462–468. (2004)

  19. 19.

    Radon, J.: On the determination of functions from their integral values along certain manifolds. IEEE Trans. Med. Imaging 5, 170–176 (1986)

    Article  Google Scholar 

  20. 20.

    Venturas, S., Flaounas, I.: Study of Radon transformation and application of its inverse to NMR. Algorithms Mol. Biol. 4, (2005)

  21. 21.

    Jadhav, D.V., Holambe, R.S.: Feature extraction using Radon and wavelet transforms with application to face recognition. Neurocomputing 72, 1951–1959 (2009)

    Article  Google Scholar 

  22. 22.

    Mandelbrot, B.B., Pignoni, R.: The Fractal Geometry of Nature, vol. 173. WH Freeman, New York (1983)

    Google Scholar 

  23. 23.

    Verghese, G.C., Oppenheim, A.V.: Signals, Systems, and Inference. Pearson, London (2010)

    Google Scholar 

  24. 24.

    Rosenstein, M.T., Collins, J.J., De Luca, C.J.: A practical method for calculating largest Lyapunov exponents from small data sets. Phys. D 65, 117–134 (1993)

    MathSciNet  Article  MATH  Google Scholar 

  25. 25.

    Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., Kurths, J.: Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Rev. E 66, 026702 (2002)

    Article  MATH  Google Scholar 

  26. 26.

    Zbilut, J.P., Webber, C.L.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 171, 199–203 (1992)

    Article  Google Scholar 

  27. 27.

    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)

    Article  Google Scholar 

  28. 28.

    Tsallis, C.: Possible generalization of Boltzmann–Gibbs statistics. J. Stat. Phys. 52, 479–487 (1988)

    MathSciNet  Article  MATH  Google Scholar 

  29. 29.

    Acharya, U.R., Faust, O., Sree, V., Swapna, G., Martis, R.J., Kadri, N.A., Suri, J.S.: Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Comput. Methods Progr. Biomed. 113(1), 55–68 (2014)

    Article  Google Scholar 

  30. 30.

    Acharya, U.R., Sree, S.V., Chattopadhyay, S., Suri, J.S.: Automated diagnosis of normal and alcoholic EEG signals. Int. J. Neural Syst. 22(3), 1250011 (2012)

    Article  Google Scholar 

  31. 31.

    Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S., Koh, J.E.W.: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl.-Based Syst. 88, 85–96 (2015)

    Article  Google Scholar 

  32. 32.

    Acharya, R., Faust, O., Kadri, N.A., Suri, J.S., Yu, W.: Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput. Biol. Med. 43(10), 1523–1529 (2013)

    Article  Google Scholar 

  33. 33.

    Chen, J., Li, G.: Tsallis wavelet entropy and its application in power signal analysis. Entropy 16, 3009–3025 (2014)

    Article  Google Scholar 

  34. 34.

    Webber C.L. Jr, Zbilut, J. P.: Recurrence quantification analysis of nonlinear dynamical systems. Tutor. Contemp. Nonlinear Methods Behav. Sci. pp. 26–94 (2005)

  35. 35.

    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on MACHINE LEARNING, pp. 249–256. (1992)

  36. 36.

    Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24, 289–300 (2002)

    Article  Google Scholar 

  37. 37.

    Friedman, J.H., Rafsky, L.C.: Multivariate generalizations of the Wald–Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697–717 (1979)

    MathSciNet  Article  MATH  Google Scholar 

  38. 38.

    Hoekstra, A., Duin, R.P.: On the nonlinearity of pattern classifiers. In: Proceedings of the 13th International Conference on Pattern Recognition, 1996, pp. 271–275. (1996)

  39. 39.

    Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Hoboken (2014)

    Book  MATH  Google Scholar 

  40. 40.

    Siddique, N., Adeli, H.: Neural Systems and Applications. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, pp. 159–181

  41. 41.

    McLachlan, G.: Discriminant Analysis and Statistical Pattern Recognition, vol. 544. Wiley, Hoboken (2004)

    MATH  Google Scholar 

  42. 42.

    Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158, 2134–2152 (2007)

    MathSciNet  Article  MATH  Google Scholar 

  43. 43.

    Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Słowiński R. (ed.) Intelligent Decision Support. Theory and Decision Library (Series D: System Theory, Knowledge Engineering and Problem Solving), vol. 11, pp. 203–232. Springer, Dordrecht (1992)

    Google Scholar 

  44. 44.

    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  45. 45.

    Conn, D., Ngun, T., Gang, L., Ramirez, C.: Fuzzy Forests: Extending Random Forests for Correlated, High-Dimensional, Data. UCLA Biostatistics Working Paper Series. https://escholarship.org/uc/item/55h4h0w7 (2015)

  46. 46.

    Louppe, G.: Understanding Random Forests: From Theory to Practice. https://arxiv.org/abs/1407.7502 (2014)

  47. 47.

    Horvath, S.: Weighted Network Analysis Applications in Genomics and Systems Biology. Springer, New York (2011)

    Book  Google Scholar 

  48. 48.

    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  49. 49.

    Tan, J.H., Acharya, U.R., Bhandary, S.V., Chua, K.C., Sivaprasad, S.: Segementation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017)

    Google Scholar 

  50. 50.

    Tan, J.H., Fujita, H., Sivaprasad, S., Bhandary, S.V., Rao, A.K., Chua, K.C., Acharya, U.R.: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 426, 66–76 (2017)

    Article  Google Scholar 

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Correspondence to U. Rajendra Acharya.

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Acharya, U.R., Akter, A., Chowriappa, P. et al. Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework. Int. J. Fuzzy Syst. 20, 1385–1402 (2018). https://doi.org/10.1007/s40815-018-0456-9

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Keywords

  • Ovarian cancer
  • Releif-F
  • Data complexity
  • k-NN
  • Fuzzy forest
  • Random forest