Multimedia Tools and Applications

, Volume 78, Issue 10, pp 12805–12834 | Cite as

Mammogram classification using contourlet features with forest optimization-based feature selection approach

  • Figlu MohantyEmail author
  • Suvendu Rup
  • Bodhisattva Dash
  • Banshidhar Majhi
  • M. N. S. Swamy


Breast cancer continues to be one of the major health issues across the world and it is mostly observed in females. However, the actual cause of this cancer is still an ongoing research topic. Hence, early detection and diagnosis of breast cancer are considered to be an effective and reliable solution. Mammography is one of the most efficacious medical tools for early detection of breast cancer. The radiologists identify the suspicious regions in the breast by carefully examining the mammograms. However, mammograms are sometimes difficult to analyze when the breast tissues are dense. Therefore, a computer-aided diagnosis (CAD) system is adopted which can improve the decisions of the radiologists. This paper proposes a hybrid CAD framework to classify the suspicious regions into either normal or abnormal, and further, benign or malignant. The proposed framework constitutes four computational modules, namely, ROI generation using cropping operation, texture feature extraction using contourlet transformation, a wrapper-based feature selection algorithm, namely, forest optimization algorithm to select the optimal features, and finally different classifiers like SVM, k-NN, Naïve Bayes, and C4.5 that are employed to classify the inputs into normal or abnormal, and again benign or malignant. The proposed framework is examined on two widely used standard datasets, namely, MIAS and DDSM. The performance measures are computed with respect to normal vs. abnormal, and benign vs. malignant for four different hybrid CAD models, namely, (Contourlet + FOA + SVM), (Contourlet + FOA + k-NN), (Contourlet + FOA + Naïve Bayes), and (Contourlet + FOA + C4.5). The highest classification accuracy of 100% is achieved for normal vs. abnormal classification in case of both MIAS and DDSM. The performance of the proposed hybrid scheme demonstrates its effectiveness with the other state-of-the-art schemes. Experimental results reveal that the proposed hybrid scheme is accurate and robust. Finally, the suggested scheme is considered as a reliable CAD framework to help the physicians for better diagnosis.


Mammograms Computer-aided diagnosis (CAD) Contourlet transform (CT) Forest optimization algorithm (FOA) Matthew’s correlation coefficient (MCC) Area under curve (AUC) 


  1. 1.
    Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  2. 2.
    Azar AT, El-Said SA (2013) Probabilistic neural network for breast cancer classification. Neural Comput Appl 23(6):1737–1751Google Scholar
  3. 3.
    Bamberger RH, Smith MJ (1992) A filter bank for the directional decomposition of images: Theory and design. IEEE Trans Signal Process 40(4):882–893Google Scholar
  4. 4.
    Berlin L (2014) Radiologic errors, past, present and future. Diagnosis 1(1):79–84Google Scholar
  5. 5.
    Berraho S, El Margae S, Kerroum MA, Fakhri Y (2017) Texture classification based on curvelet transform and extreme learning machine with reduced feature set. Multimed Tools Appl 76(18):18,425–18,448Google Scholar
  6. 6.
    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 154:1–14Google Scholar
  7. 7.
    Burt P, Adelson E (1983) The laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540Google Scholar
  8. 8.
    Dheeba J, Singh NA, Selvi ST (2014) Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J Biomed Inform 49:45–52Google Scholar
  9. 9.
    Do MN (2002) Directional multiresolution image representationsGoogle Scholar
  10. 10.
    Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106Google Scholar
  11. 11.
    Do Nascimento MZ, Martins AS, Neves LA, Ramos RP, Flores EL, Carrijo GA (2013) Classification of masses in mammographic image using wavelet domain features and polynomial classifier. Expert Syst Appl 40(15):6213–6221Google Scholar
  12. 12.
    El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21(12):1552–1563Google Scholar
  13. 13.
    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):123–128Google Scholar
  14. 14.
    Fu J, Lee S, Wong S, Yeh J, Wang A, Wu H (2005) Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph 29(6):419–429Google Scholar
  15. 15.
    Gedik N (2016) A new feature extraction method based on multi-resolution representations of mammograms. Appl Soft Comput 44:128–133Google Scholar
  16. 16.
    Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687Google Scholar
  17. 17.
    Ghaemi M, Feizi-Derakhshi MR (2016) Feature selection using forest optimization algorithm. Pattern Recogn 60:121–129Google Scholar
  18. 18.
    Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified pcnn. Comput Methods Prog Biomed 130:31–45Google Scholar
  19. 19.
    Gupta S, Chyn PF, Markey MK (2006) Breast cancer cadx based on bi-radsdescriptors from two mammographic views. Med Phys 33(6):1810–1817Google Scholar
  20. 20.
    Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography, Medical Physics Publishing, pp 212–218Google Scholar
  21. 21.
    Jona J, Nagaveni N (2012) A hybrid swarm optimization approach for feature set reduction in digital mammograms. WSEAS Trans Inf Sci Appl 9:340–349Google Scholar
  22. 22.
    de Lima SM, da Silva-Filho AG, dos Santos WP (2016) Detection and classification of masses in mammographic images in a multi-kernel approach. Comput Methods Prog Biomed 134:11–29Google Scholar
  23. 23.
    Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim Biophys Acta (BBA)-Protein Struct 405(2):442–451Google Scholar
  24. 24.
    Mohamed H, Mabrouk MS, Sharawy A (2014) Computer aided detection system for micro calcifications in digital mammograms. Comput Methods Prog Biomed 116(3):226–235Google Scholar
  25. 25.
    Pawar MM, Talbar SN (2016) Genetic fuzzy system (gfs) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect Sci 8:247–250Google Scholar
  26. 26.
    Phadke AC, Rege PP (2016) Fusion of local and global features for classification of abnormality in mammograms. Sādhanā 41(4):385–395MathSciNetzbMATHGoogle Scholar
  27. 27.
    Prathibha B, Sadasivam V (2010) Breast tissue characterization using variants of nearest neighbour classifier in multi texture domain. IE (I) J 91:7–13Google Scholar
  28. 28.
    Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, AmsterdamGoogle Scholar
  29. 29.
    Rish I (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3. IBM, pp 41–46Google Scholar
  30. 30.
    Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and cnn segmentation. Expert Syst Appl 42(3):990–1002Google Scholar
  31. 31.
    Roy D, Murty KSR, Mohan CK (2015) Feature selection using deep neural networks. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–6Google Scholar
  32. 32.
    Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA: a Cancer J Clin 65(1):5–29Google Scholar
  33. 33.
    Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S et al (1994) The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol 1069, pp 375–378Google Scholar
  34. 34.
    Verma B, Zakos J (2001) A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inf Technol Biomed 5(1):46–54Google Scholar
  35. 35.
    Vetterli M (1984) Multi-dimensional sub-band coding: Some theory and algorithms. Signal Process 6(2):97–112MathSciNetGoogle Scholar
  36. 36.
    WHO (2013) Latest world cancer statistics global cancer burden rises to 14.1 million new cases in 2012: Marked increase in breast cancers must be addressed. international agency for research on cancer and others. World Health Organization, pp 12Google Scholar
  37. 37.
    Xie W, Li Y, Ma Y (2016) Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173:930–941Google Scholar
  38. 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 46:95–107Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Figlu Mohanty
    • 1
    Email author
  • Suvendu Rup
    • 1
  • Bodhisattva Dash
    • 1
  • Banshidhar Majhi
    • 2
  • M. N. S. Swamy
    • 3
  1. 1.Department of Computer Science and Engineering, Image and Video Processing LaboratoryInternational Institute of Information TechnologyBhubaneswarIndia
  2. 2.Department of Computer Science and Engineering, Pattern Recognition Research LaboratoryNational Institute of TechnologyRourkelaIndia
  3. 3.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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