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Breast density analysis based on glandular tissue segmentation and mixed feature extraction

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Abstract

Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose a classification system for breast density, which can appropriately segment the glandular tissue from the whole breast and to achieve a better classification result. A new threshold method is applied to segment the breast glandular tissue. The gray level co-occurrence matrix (GLCM) is implemented to extract the texture features of the glandular tissue. Meanwhile, we obtain three statistical features (mean, skewness, kurtosis). In addition, the calculated breast density that is served as a new feature is added to the feature vectors. The mixed feature vectors are classified by Support Vector Machine (SVM) and Ultimate Learning Machine (ELM). Ten-fold cross-validation is used to verify the classifier performance. The system using the SVM achieves 96.19% accuracy for three density types in the MIAS database and achieves 96.35% accuracy of four density types in the DDSM database. The accuracy in the database mixed with the local database was 95.01% and there are three density types in the mixed database. The experimental results indicate that the system proposed has a better performance in breast density classification. The system proposed in this paper can be considered to help the physician to classify breast density.

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Abbreviations

DDSM:

Digital database for screening mammography

MIAS:

Mammographic image analysis society

CAD:

Computer-aided diagnosis

ROI:

Region of interest

SWB:

Segmented whole breast

SGT:

Segmented glandular tissue

BI-RADS:

Breast imaging-reporting and data system

GLCM:

Gray level co-occurrence matrix

References

  1. Anguita D, Ridella S, Rivieccio F (2005) K-fold generalization capability assessment for support vector classifiers. in Proceedings. 2005 IEEE International Joint Conference on Neural Networks. 2005, IEEE

  2. Anguita D et al (2009) K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines. in DMIN

  3. Anthony G, Gregg H, Tshilidzi M (2007) Image classification using SVMs: one-against-one vs one-against-all. arXiv preprint arXiv:0711.2914

  4. Arnau OIM, Jordi FIB, Zwiggelaar R (2005) Automatic classification of breast density. Lect Notes Comput Sci 3523:431–438

    Article  Google Scholar 

  5. Arnau O et al (2015) Breast Density Analysis Using an Automatic Density Segmentation Algorithm. J Digit Imaging 28(5):604–612

    Article  Google Scholar 

  6. Blot L, Zwiggelaar R (2001) Background texture extraction for the classification of mammographic parenchymal patterns. Miua:145–148

  7. Bosch A, et al (2006) Modeling and Classifying Breast Tissue Density in Mammograms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition

  8. Bouyahia S, Mbainaibeye J, Ellouze N (2004) Computer-aided diagnosis of mammographic images. First International Symposium on Control, Communications and Signal Processing 2004

  9. Bovis K, Singh S (2002) Classification of Mammographic Breast Density Using a Combined Classifier Paradigm. International Workshop on Digital Mammography:177–180

  10. Breast Cancer U.K. (2017) Key facts about breast cancer. http://www.breastcanceruk.org.uk/

  11. Breast Cancer: U.S. Breast Cancer Statistics (2017). http://www.breastcancer.org/symptoms/understand_bc/statistics. Accessed 10 Mar 2017.

  12. Cancer Facts & Figures (2017). https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2017.html

  13. Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. 2(3): p. 1–27.

  14. Chen Z, Denton E, Zwiggelaar R (2011) Local Feature Based Mammographic Tissue Pattern Modelling and Breast Density Classification. In: 2011 4th international conference on biomedical engineering and informatics

  15. Chen, D., et al. (2013) The Correlation Analysis between Breast Density and Cancer Risk Factor in Breast MRI Images. In: 2013 International Symposium on Biometrics and Security Technologies

  16. Chen W et al (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66(2):115

    Article  Google Scholar 

  17. Clausi DA (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens 28(1):45–62

    Article  Google Scholar 

  18. Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  19. Elmoufidi A et al (2015) Automatically density based breast segmentation for mammograms by using dynamic K-means algorithm and Seed Based Region Growing. Instrumentation and Measurement Technology Conference

  20. Elshinawy M et al (2011) Effect of breast density in selecting features for normal mammogram detection. In: IEEE International Symposium on Biomedical Imaging: From Nano To Macro

  21. Engeland Sv et al (2006) Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging 25(3):273–282

    Article  Google Scholar 

  22. Giuliano V, Giuliano C (2013) Volumetric Breast Ultrasound as a Screening Modality in Mammographically Dense Breasts. ISRN Radiology 2013:235270–235270

    Article  Google Scholar 

  23. Gonzalez RC, Woods RE (2007) Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle river, pp 1160–1165

    Google Scholar 

  24. Gualtieri JA, Cromp RF (1999) Support vector machines for hyperspectral remote sensing classification. in 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics.

  25. Gubern-Mérida A et al (2015) Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework. IEEE Journal of Biomedical and Health Informatics 19(1):349–357

    Article  Google Scholar 

  26. Guo YN et al (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Prog Biomed 130(C):31–45

    Article  Google Scholar 

  27. Haralick RM, Shanmugam K, Dinstein IH, Haralick RM, Shanmuga K (1973) Dinstein ITextural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  28. He W et al (2011) Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments. Biomedical Signal Processing and Control 6(3):321–329

    Article  Google Scholar 

  29. Heath M et al (2001) The Digital Database for Screening Mammography

  30. Hsu CW (2002) and C.J. Lin, A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(4):1026

    Article  Google Scholar 

  31. Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240

    Article  Google Scholar 

  32. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  33. Kohavi RA (1995) Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: International Joint Conference on Artificial Intelligence

  34. Kumar I, Bhadauria HS, Virmani J (2015) Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification. Procedia Computer Science 70:76–84

    Article  Google Scholar 

  35. Kumar I, Virmani J, Bhadauria HS (2015) A review of breast density classification methods. In: International Conference on Computing for Sustainable Global Development

  36. Kumar I et al (2017) A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybernetics & Biomedical Engineering 37(1):217–228

    Article  Google Scholar 

  37. Liberman L, Menell JH (2002) Breast imaging reporting and data system (BI-RADS). Radiol Clin N Am 40(3):409–430

    Article  Google Scholar 

  38. Lin SW et al (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8(4):1505–1512

    Article  Google Scholar 

  39. Liu L, Wang J, He K (2010) Breast density classification using histogram moments of multiple resolution mammograms. International Conference on Biomedical Engineering and Informatics

  40. Machida Y et al (2015) Breast density: the trend in breast cancer screening. Breast Cancer 22(3):253–261

    Article  Google Scholar 

  41. Malkov S et al (2016) Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Research : BCR 18(1):122

    Article  Google Scholar 

  42. Manduca A et al (2009) Texture features from mammographic images and risk of breast cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research. American Society of Preventive Oncology 18(3):837–845

    Google Scholar 

  43. Marchette DJ, Lorey RA, Priebe CE (1997) An analysis of local feature extraction in digital mammography. Pattern Recogn 30(9):1547–1554

    Article  Google Scholar 

  44. Martin JE, Moskowitz M (1979) and Milbrath, Breast cancer missed by mammography. Am J Roentgenol 132(5):737–739

    Article  Google Scholar 

  45. Muhimmah I (2006) Mammographic Density Classification Using Multi Resolution Histogram Information

  46. Mustra M, Grgic M (2013) Dense tissue segmentation in digitized mammograms. in Elmar, 2013 International Symposium

  47. Mustra M, Grgic M, Delac K (2010) Feature selection for automatic breast density classification. in ELMAR, 2010 Proceedings

  48. Nagata C et al (2005) Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 92:2102

    Article  Google Scholar 

  49. Oliveira JEED, Araújo ADA, Deserno TM (2011) Content-based image retrieval applied to BI-RADS tissue classification in screening mammography. World Journal of Radiology 3(1):24

    Article  Google Scholar 

  50. Oliver A et al (2008) A novel breast tissue density classification methodology. IEEE Transactions on Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine & Biology Society 12(1):55–65

    Article  Google Scholar 

  51. Otsu N (1979) A Threshold Selection Method From Grey Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9:62–66

    Article  Google Scholar 

  52. Petroudi S, Kadir T, Brady M (2003) Automatic classification of mammographic parenchymal patterns: a statistical approach. In Engineering in Medicine and Biology Society, 2003. Proceedings of the International Conference of the IEEE

  53. Petroudi S et al (2013) Investigation of AM-FM methods for mammographic breast density classification. IEEE International Conference on Bioinformatics and Bioengineering

  54. Pham DL, Xu C, Prince JL (2000) Current Methods in Medical Image Segmentation. Annu Rev Biomed Eng 2(1):315–337

    Article  Google Scholar 

  55. Rampun A, et al (2017) Breast Density Classification Using Multiresolution Local Quinary Patterns in Mammograms

  56. Remes V Haindl M (2015) Classification of breast density in X-ray mammography. International Workshop on Computational Intelligence for Multimedia Understanding

  57. Schölkopf B, Smola A (2001) Learning with kernels : support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, pp 781–781

    Google Scholar 

  58. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30

    Article  Google Scholar 

  59. Sivaramakrishna R et al (2001) Automatic segmentation of mammographic density. Acad Radiol 8(3):250

    Article  Google Scholar 

  60. Soh L-K, Tsatsoulis C (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795

    Article  Google Scholar 

  61. Sohn G et al (2014) Reliability of the percent density in digital mammography with a semi-automated thresholding method. J Breast Cancer 17(2):174–179

    Article  Google Scholar 

  62. Strand F et al (2016) Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study. Breast Cancer Res 18(1):100

    Article  Google Scholar 

  63. Subashini TS, Ramalingam V, Palanivel S (2010) Automated assessment of breast tissue density in digital mammograms. Computer Vision & Image Understanding 114(1):33–43

    Article  Google Scholar 

  64. Suckling J, Parker J, Dance DR (1994) Themammographic image analysis society digital mammogram database. in Int Work on Dig Mammography

  65. Tzikopoulos SD et al (2011) A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Methods Prog Biomed 102(1):47–63

    Article  Google Scholar 

  66. Van Engeland S et al (2006) Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging 25(3):273

    Article  Google Scholar 

  67. Wang J et al (2017) Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 30(2):215–227

    Article  Google Scholar 

  68. Wei C, Gwo C, Li Y (2015) A Framework of Breast Density Estimation System for Breast Magnetic Resonance Images. In 2015 2nd International Conference on Information Science and Control Engineering

  69. Wei CH, Gwo CY, Li Y (2015) A Framework of Breast Density Estimation System for Breast Magnetic Resonance Images. International Conference on Information Science and Control Engineering

  70. Wolfe JN (1976) Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5):2486

    Article  Google Scholar 

  71. Yaffe MJ (2008) Mammographic density. Measurement of mammographic density. Breast Cancer Research Bcr 10(3):209

    Article  Google Scholar 

  72. Zhengyou L, Xiaoshan G (2015) A segmentation method for mammogram x-ray image based on image enhancement with wavelet fusion

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Acknowledgements

This work is jointly supported by the Natural Science Foundation of Gansu Province (No.18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No.lzujbky-2017-it72 and No.lzujbky-2018-it61).

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  1. School of Information Science Engineering, Lanzhou University, No. 222 Tianshui South Road, Lanzhou, 730000, Gansu, China

    Xiaonan Gong, Zhen Yang, Deyuan Wang, Yunliang Qi, Yanan Guo & Yide Ma

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  1. Xiaonan Gong
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Correspondence to Yide Ma.

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Gong, X., Yang, Z., Wang, D. et al. Breast density analysis based on glandular tissue segmentation and mixed feature extraction. Multimed Tools Appl 78, 31185–31214 (2019). https://doi.org/10.1007/s11042-019-07917-2

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