Computer aided detection of mammographic mass using exact Gaussian–Hermite moments

  • Mohamed Meselhy Eltoukhy
  • Mohamed Elhoseny
  • Khalid M. Hosny
  • Amit Kumar Singh
Original Research
  • 20 Downloads

Abstract

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods.

Keywords

Computer aided diagnose Gaussian–Hermite Breast cancer Health informatics 

Notes

Acknowledgements

The IRMA dataset used in this study was used by courtesy of Thomas M. Deserno, Department of Medical Informatics, Aachen, Germany. In addition, we would like to thank Dr. Mohamed Tahoun and Dr. S. J. Gardezi for their discussion and invaluable comments.

References

  1. Abdelaziz A, Elhoseny M, Salama AS, Riad AM (2018) A machine learning model for improving healthcare services on cloud computing environment. Measurement 119:117–128CrossRefGoogle Scholar
  2. Abdelwahed NM, Eltoukhy MM, Wahed ME (2015) Computer aided system for breast cancer diagnosis in ultrasound images. J Ecol Health Environ 3(3):71–76Google Scholar
  3. American Cancer Society (2017) Cancer facts & figures 2017. American Cancer Society, AtlantaGoogle Scholar
  4. Beura S, Majhi B, Dash R (2015) Mammogram classification using two-dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154:1–14CrossRefGoogle Scholar
  5. Bruno DOT, Nascimento do, Ramos MZ, Batista RP, Neves VR, L. A., & Martins AS (2016) LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Syst Appl 55:329–340CrossRefGoogle Scholar
  6. Chakraborty J, Midya A, Rabidas R (2018) Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns. Expert Syst Appl 99:168–179CrossRefGoogle Scholar
  7. Cheng HD, Cai X, Chen X, Hu L, Lou X (2003) Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit 36(12):2967–2991CrossRefMATHGoogle Scholar
  8. Cvetković J, Nenadović M (2016) Depression in breast cancer patients. Psychiatry Res 240:343–347CrossRefGoogle Scholar
  9. Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2017) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-017-0659-1 Google Scholar
  10. Dhahbi S, Barhoumi W, Zagrouba E (2015) Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput Biol Med 64:79–90CrossRefGoogle Scholar
  11. Elhoseny M, Ramírez-González G, Abu-Elnasr OM, Shawkat SA, Arunkumar N, Farouk A (2018) Secure medical data transmission model for IoT-based healthcare systems. IEEE Access 6:20596–20608.  https://doi.org/10.1109/ACCESS.2018.2817615 CrossRefGoogle Scholar
  12. Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K, Sangaiah AK (2018) A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Future Gener Comput Syst.  https://doi.org/10.1016/j.future.2018.03.005 Google Scholar
  13. Eltoukhy MM, Faye I (2013) An adaptive threshold method for mass detection in mammographic images. In: 2013 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, Piscataway, pp 374–378Google Scholar
  14. Eltoukhy MM, Faye I (2014) An optimized feature selection method for breast cancer diagnosis in digital mammogram using multiresolution representation. Appl Math Inf Sci 8(6):2921CrossRefGoogle Scholar
  15. Eltoukhy MM, Faye I, Samir BB (2010a) Curvelet based feature extraction method for breast cancer diagnosis in digital mammogram. In: 2010 International conference on intelligent and advanced systems (ICIAS). IEEE, Piscataway, pp 1–5Google Scholar
  16. Eltoukhy MM, Faye I, Samir BB (2010b) A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput Biol Med 40(4):384–391CrossRefGoogle Scholar
  17. Eltoukhy MM, Faye I, Samir BB (2012) A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Comput Biol Med 42(1):123–128CrossRefGoogle Scholar
  18. Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley, New YorkCrossRefMATHGoogle Scholar
  19. Gedik N (2016) A new feature extraction method based on multi-resolution representations of mammograms. Appl Soft Comput 44:128–133CrossRefGoogle Scholar
  20. Hastie T, Tibshirani R, Friedman J (2016) The elements of statistical, data mining, inference, and prediction, 2nd edn. Springer Science, BerlinMATHGoogle Scholar
  21. Hosny KM (2007) Exact and fast computation of geometric moments for gray level images. Appl Math Comput 189(2):1214–1222MathSciNetMATHGoogle Scholar
  22. Hosny KM (2010) Robust template matching using orthogonal Legendre moment invariants. J Comput Sci 6(10):1083CrossRefGoogle Scholar
  23. Hosny KM (2012) Fast computation of accurate Gaussian–Hermite moments for image processing applications. Digit Signal Process 22(3):476–485MathSciNetCrossRefGoogle Scholar
  24. Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231CrossRefGoogle Scholar
  25. Khan S, Hussain M, Aboalsamh H, Mathkour H, Bebis G, Zakariah M (2016) Optimized Gabor features for mass classification in mammography. Appl Soft Comput 44:267–280CrossRefGoogle Scholar
  26. Lajevardi SM, Hussain ZM (2010) Higher order orthogonal moments for invariant facial expression recognition. Digit Signal Process 20(6):1771–1779CrossRefGoogle Scholar
  27. Raghavendra U, Acharya UR, Fujita H, Gudigar A, Tan JH, Chokkadi S (2016) Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput 46:151–161CrossRefGoogle Scholar
  28. Rahman SM, Reza MM, Hasani QZ (2013) Low-complexity iris recognition method using 2D Gauss–Hermite moments. In: 2013 8th International symposium on image and signal processing and analysis (ISPA). IEEE, Piscataway, pp 142–146Google Scholar
  29. Sajjad M, Nasir M, Muhammad K, Khan S, Jan Z, Sangaiah AK, … Baik SW (2017) Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Gener Comput SystGoogle Scholar
  30. Sharma S, Khanna P (2015) Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J Digit Imaging 28(1):77–90CrossRefGoogle Scholar
  31. Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6:10269–10278CrossRefGoogle Scholar
  32. Shen J, Shen W, Shen D (2000) On geometric and orthogonal moments. Int J Pattern Recognit Artif Intell 14(07):875–894CrossRefMATHGoogle Scholar
  33. Wang L, Dai M (2007) Application of a new type of singular points in fingerprint classification. Pattern Recognit Lett 28(13):1640–1650CrossRefGoogle Scholar
  34. Wu Y, Shen J (2005) Properties of orthogonal Gaussian–Hermite moments and their applications. EURASIP J Appl Signal Process 2005, 588–599Google Scholar
  35. Yang B, Dai M (2011) Image analysis by Gaussian–Hermite moments. Signal Process 91(10):2290–2303CrossRefMATHGoogle Scholar
  36. Yang B, Li G, Zhang H, Dai M (2011) Rotation and translation invariants of Gaussian–Hermite moments. Pattern Recognit Lett 32(9):1283–1298CrossRefGoogle Scholar
  37. Yang B, Flusser J, Suk T (2015) Design of high-order rotation invariants from Gaussian–Hermite moments. Sig Process 113:61–67CrossRefGoogle Scholar
  38. Zyout I, Czajkowska J, Grzegorzek M (2015) Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput Med Imaging Graph 46:95–107CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science Department, Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt
  2. 2.Faculty of Computers and InformationMansoura UniversityEl MansûraEgypt
  3. 3.Department of Information Technology, Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt
  4. 4.Jaypee University of Information Technology (JUIT)SolanIndia

Personalised recommendations