Advertisement

Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach

  • Figlu MohantyEmail author
  • Suvendu Rup
  • Bodhisattva Dash
  • Banshidhar Majhi
  • M. N. S. Swamy
Original Article
  • 57 Downloads

Abstract

This paper proposes an improved computer-aided diagnosis model to identify mammographic images as normal or abnormal, and further, benign or malignant. The proposed scheme employs all the steps associated with other classification schemes; however, the contribution of the suggested scheme is fourfold. Initially, a fusion-based feature extraction method is employed to obtain the features using a combination of 2-D block discrete wavelet transform (2D-BDWT) and gray-level co-occurrence matrix (GLCM). Next, principal component analysis (PCA) is utilized to reduce the large dimension of the feature vector. Furthermore, to select the most optimal features from the reduced set of features, forest optimization algorithm (FOA) is used. The FOA-based feature selection algorithm is utilized as a wrapper-based technique which includes both feature selection and classification. In the proposed framework, several classifiers, namely support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (C4.5), are applied. The proposed method is compared with the benchmark schemes on two standard datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). Simulation results and analysis confirm that the proposed scheme brings potential improvements with respect to classification accuracy, sensitivity, specificity, area under curve, F-score, and Matthews correlation coefficient. The classification accuracy is measured with respect to normal versus abnormal and further, benign versus malignant. The proposed scheme with different combinations of classifiers, namely 2D-BDWT + GLCM + PCA + FOA + SVM, 2D-BDWT + GLCM + PCA + FOA + k-NN, and 2D-BDWT + GLCM + PCA + FOA + C4.5, achieves a maximum classification accuracy of 100% for both the MIAS and DDSM datasets.

Keywords

Computer-aided diagnosis Discrete wavelet transform (DWT) Forest optimization algorithm (FOA) Matthews correlation coefficient (MCC) 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Abubacker NF, Azman A, Doraisamy S, Murad MAA (2017) an integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification. Neural Comput Appl 28(12):3967–3980CrossRefGoogle Scholar
  2. 2.
    Aminikhanghahi S, Shin S, Wang W, Jeon SI, Son SH (2017) a new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification. Multimed Tools Appl 76(7):10,191–10,205CrossRefGoogle Scholar
  3. 3.
    Azar AT, El-Said SA (2013) Probabilistic neural network for breast cancer classification. Neural Comput Appl 23(6):1737–1751CrossRefGoogle Scholar
  4. 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. 5.
    Cheng H, Shi X, Min R, Hu L, Cai X, Du H (2006) Approaches for automated detection and classification of masses in mammograms. Pattern Recognit 39(4):646–668CrossRefGoogle Scholar
  6. 6.
    Christopher MB (2016) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  7. 7.
    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 Programs Biomed 134:11–29CrossRefGoogle Scholar
  8. 8.
    Dhahbi S, Barhoumi W, Kurek J, Swiderski B, Kruk M, Zagrouba E (2018) false-positive reduction in computer-aided mass detection using mammographic texture analysis and classification. Comput Methods Programs Biomed 160:75–83CrossRefGoogle Scholar
  9. 9.
    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 49:45–52CrossRefGoogle Scholar
  10. 10.
    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):1552–1563CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng KH (2013) Computer-aided breast cancer detection using mammograms: a review. IEEE Rev Biomed Eng 6:77–98CrossRefGoogle Scholar
  13. 13.
    Gedik N (2016) A new feature extraction method based on multi-resolution representations of mammograms. Appl Soft Comput 44:128–133CrossRefGoogle Scholar
  14. 14.
    Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687CrossRefGoogle Scholar
  15. 15.
    Ghaemi M, Feizi-Derakhshi MR (2016) Feature selection using forest optimization algorithm. Pattern Recognit 60:121–129CrossRefGoogle Scholar
  16. 16.
    Gonzalez RC, Woods RE (2012) Histogram processing. In: Digital image processing, 3rd edn. PHEI, Beijing, pp 162–165Google Scholar
  17. 17.
    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 Programs Biomed 130:31–45CrossRefGoogle Scholar
  18. 18.
    Haralick RM, Shanmugam K et al (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    International Agency for Research on Cancer (2012) The GLOBOCAN project: cancer incidence and mortality worldwide in 2012. http://globocan.iarc.fr/. Accessed 13 Jan 2010
  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.
    Kolb TM, Lichy J, Newhouse JH (2002) Comparison of the performance of screening mammography, physical examination, and breast us and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 225(1):165–175CrossRefGoogle Scholar
  23. 23.
    Lewis C (1999) Fda sets higher standards for mammography. FDA Consum 33(1):13–17Google Scholar
  24. 24.
    Liu N, Qi ES, Xu M, Gao B, Liu GQ (2019a) A novel intelligent classification model for breast cancer diagnosis. Inf Process Manag 56(3):609–623CrossRefGoogle Scholar
  25. 25.
    Liu X, Zhu T, Zhai L, Liu J (2019b) Mass classification of benign and malignant with a new twin support vector machine joint \(l_{2,1}\)-norm. Int J Mach Learn Cybern 10(1):155–171CrossRefGoogle Scholar
  26. 26.
    Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct 405(2):442–451CrossRefGoogle Scholar
  27. 27.
    Mohamed H, Mabrouk MS, Sharawy A (2014) Computer aided detection system for micro calcifications in digital mammograms. Comput Methods Programs Biomed 116(3):226–235CrossRefGoogle Scholar
  28. 28.
    Pawar MM, Talbar SN (2016) Genetic fuzzy system (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect Sci 8:247–250CrossRefGoogle Scholar
  29. 29.
    Rampun A, Scotney B, Morrow P, Wang H, Winder J (2018) Breast density classification using local quinary patterns with various neighbourhood topologies. J Imaging 4(1):14CrossRefGoogle Scholar
  30. 30.
    Rouhi R, Jafari M (2016) Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst Appl 46:45–59CrossRefGoogle Scholar
  31. 31.
    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):990–1002CrossRefGoogle Scholar
  32. 32.
    Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA Cancer J Clin 65(1):5–29CrossRefGoogle 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. Exerpta Med Int Congr Ser 1069:375–378Google Scholar
  34. 34.
    Thawkar S, Ingolikar R (2018a) Classification of masses in digital mammograms using biogeography-based optimization technique. J King Saud Univ Comput Inf SciGoogle Scholar
  35. 35.
    Thawkar S, Ingolikar R (2018b) Classification of masses in digital mammograms using firefly based optimization. Int J Image Graph Signal Process 10(2):25–33CrossRefGoogle Scholar
  36. 36.
    Wang S, Rao RV, Chen P, Zhang Y, Liu A, Wei L (2017) Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm. Fundam Inform 151(1–4):191–211MathSciNetCrossRefGoogle Scholar
  37. 37.
    Xie W, Li Y, Ma Y (2016) Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173:930–941CrossRefGoogle Scholar
  38. 38.
    Yang L, Xu Z (2019) Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning. Int J Mach Learn Cybern 10(3):591–601CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

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

Personalised recommendations