Skip to main content
SpringerLink
Log in

Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing.

Materials and methods

We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student’s t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods.

Results

The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity—from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification.

Conclusion

The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

References

  1. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics. Cancer J Clin 66(1):7–30

    Article  Google Scholar 

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 68:394–424

    Article  Google Scholar 

  3. Nelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L (2016) Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 U.S. preventive services task force recommendation. Ann Intern Med 164(4):244–255

    Article  Google Scholar 

  4. Rebolj M, Assi V, Brentnall A, Parmar D, Duffy SW (2018) Addition of ultrasound to mammography in the case of dense breast tissue: systematic review and meta-analysis. Br J Cancer 118:1559–1570. https://doi.org/10.1038/s41416-018-0080-3

    Article  PubMed  PubMed Central  Google Scholar 

  5. Brem RF, Lenihan MJ, Lieberman J, Torrente J (2015) Screening breast ultrasound: past, present, and future. Am J Roentgenol 204(2):234–240

    Article  Google Scholar 

  6. Houssami N., Irwig L., Owen U.N.G. (2005) Review of complex breast cyst: Implications for cancer detection and clinical practice. ANZ, Surg, 1080–1085

  7. Gokhale S (2009) Ultrasound characterization of breast masses. Indian J Radiol Imaging 19(3):242–247. https://doi.org/10.4103/0971-3026.54878

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chang RF, Wu WJ, Moon WK, Chen DR (2005) Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 89(2):179–185. https://doi.org/10.1007/s10549-004-2043-z

    Article  PubMed  Google Scholar 

  9. Karimi B., Krzyzak A. (2014) Computer-aided system for automatic classification of suspicious lesions in breast ultrasound images. In: Conference: international conference on artificial intelligence and soft computing, pp 131–142. https://doi.org/10.1007/978-3-319-07176-3_12

  10. Sidiropoulos KP, Kostopoulos SA, Glotsos DT, Athanasiadis EI, Dimitropoulos ND, Stonham JT, Cavouras DA (2013) Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images. Int J Comput Assist Radiol Surg 8(4):547–560. https://doi.org/10.1007/s11548-013-0813-y

    Article  PubMed  Google Scholar 

  11. Prabhakar T, Poonguzhali S (2018) Assessment of texture feature extraction to classify the benign and malignant lesions from breast ultrasound images. Artificial intelligence and evolutionary computations in engineering systems. Adv Intell Syst Comput 668:725–732. https://doi.org/10.1007/978-981-10-7868-2_69

    Article  Google Scholar 

  12. Ilesanmi AE, Chaumrattanakul U, Makhanov SS (2021) Methods for the segmentation and classification of breast ultrasound images: a review. J Ultrasound. https://doi.org/10.1007/s40477-020-00557-5

    Article  PubMed  Google Scholar 

  13. Egoshin IA, Pasynkov DV, Kolchev AA, Kliouchkin IV, Pasynkova OO (2020) Segmentation of breast focal lesions on the ultrasound image. Biomed Eng 54(2):99–103. https://doi.org/10.1007/s10527-020-09982-6

    Article  Google Scholar 

  14. Garra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK (1993) Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. Ultrason Imaging 15(4):267–285

    Article  CAS  Google Scholar 

  15. Sivaramakrishna R, Powell KA, Lieber ML, Chilcote WA, Shekhar R (2002) Texture analysis of lesions in breast ultrasound images. Comput Med Imaging Graph 26(5):303–307

    Article  Google Scholar 

  16. Chen DR, Chang RF, Kuo WJ, Chen MC, Huang YL (2002) Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol 28(10):1301–1310

    Article  Google Scholar 

  17. Chen SJ, Cheng KS, Dai YC, Sun YN, Chen YT, Chang KY, Hsu WC, Chang TW (2005) The representations of sonographic image texture for breast cancer using co-occurrence matrix. J Med Biol Eng 25(4):193–199

    Google Scholar 

  18. Hongjiao G, Yingtao Z, Heng-Da C, Xianglong T (2020) Bounded–abstaining classification for breast tumors in imbalanced ultrasound images. Int J Appl Math Comput Sci 30(2):325–336. https://doi.org/10.34768/amcs-2020-0025

    Article  Google Scholar 

  19. Sadeghi-Naini A, Suraweera H, Tran WT, Hadizad F, Bruni G, Rastegar RF, Curpen B, Czarnota GJ (2017) Breast-lesion characterization using textural features of quantitative ultrasound parametric maps. Sci Rep 7(1):136–138. https://doi.org/10.1038/s41598-017-13977-x

    Article  CAS  Google Scholar 

  20. Yanyan Yu, Yang X, Jieyu C, Bernard C (2018) Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images. Comput Biol Med 93:31–46. https://doi.org/10.1016/j.compbiomed.2017.12.006

    Article  Google Scholar 

  21. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473

    Article  Google Scholar 

  22. Haralick RM, Shanugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybernet SMC 3(6):610–621

    Article  Google Scholar 

  23. Hsu SM, Kuo WH, Kuo FC, Liao YY (2019) Breast tumor classification using different features of quantitative ultrasound parametric images. Int J CARS 14:623–633. https://doi.org/10.1007/s11548-018-01908-8

    Article  Google Scholar 

  24. Liao YY, Wu JC, Li CH, Yeh CK (2011) Texture feature analysis for breast ultrasound image enhancement. Ultrason Imaging 33(4):264–278

    Article  Google Scholar 

  25. Mendelson EB, Baum JK, Berg WA, Merritt CRB, Rubin E (2003) Breast imaging reporting and data system, BI-RADS: ultrasound, 1st edn. American College of Radiology, Reston

    Google Scholar 

  26. Berg WA, Campassi CI, Ioffe OB (2003) Cystic lesions of the breast: sonographic-pathologic correlation. Radiology 227(1):183–191

    Article  Google Scholar 

  27. Wei M, Yongzhao Du, Xiuming Wu, Qichen Su, Zhu J, Zheng L, Lv G, Zhuang J (2020) A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput Math Methods Med. https://doi.org/10.1155/2020/5894010

    Article  PubMed  PubMed Central  Google Scholar 

  28. Abdel-Nasser M, Melendez J, Moreno A, Omer O, Puig D (2016) Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell 59:84–92. https://doi.org/10.1016/j.engappai.2016.12.019

    Article  Google Scholar 

  29. Daoud MI, Bdair TM, Al-Najar M, Alazrai R (2016) A Fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses. Comput Math Methods Med. https://doi.org/10.1155/2016/6740956

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dhanachandra N, Chanu YJ (2020) An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Multimedia Tools Appl 79:18839–18858. https://doi.org/10.1007/s11042-020-08699-8

    Article  Google Scholar 

  31. Choudhry M, Kapoor R (2016) Performance analysis of fuzzy C-means clustering methods for MRI image segmentation. Procedia Comput Sci 89:749–758. https://doi.org/10.1016/j.procs.2016.06.052

    Article  Google Scholar 

  32. Yang Y, Zhang F, Zheng C, Lin P (2005) Unsupervised image segmentation using penalized fuzzy clustering algorithm. In Gallagher M, Hogan JP, Maire F Intelligent data engineering and automated learning—IDEAL 2005. Lecture notes in computer science (3578), Springer, Berlin. https://doi.org/10.1007/11508069_10

  33. Klimonda Z, Karwat P, Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Litniewski J (2019) Breast-lesions characterization using quantitative ultrasound features of peritumoral tissue. Sci Rep 9(1):7963. https://doi.org/10.1038/s41598-019-44376-z

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

This is the investigator initiated study. This study was not funded.

Author information

Authors and Affiliations

  1. Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola, 424000, Russia

    A. A. Kolchev, I. A. Egoshin & O. O. Pasynkova

  2. Kazan (Volga region) Federal University, Ministry of Education and Science of Russian Federation, 18 Kremlevskaya St., Kazan, 420008, Russia

    A. A. Kolchev

  3. Oncology Dispenser of Mari El Republic, Ministry of Health of Mari El Republic, 22 Osipenko St., Yoshkar-Ola, 424037, Russia

    D. V. Pasynkov

  4. Kazan Medical University, Ministry of Health of Russian Federation, 49 Butlerova St., Kazan, 420012, Russia

    I. V. Kliouchkin

Authors
  1. A. A. Kolchev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. V. Pasynkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. A. Egoshin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. I. V. Kliouchkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. O. O. Pasynkova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. A. Egoshin.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

This articles does not contain patient data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolchev, A.A., Pasynkov, D.V., Egoshin, I.A. et al. Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images. Int J CARS 17, 219–228 (2022). https://doi.org/10.1007/s11548-021-02522-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02522-x

Keywords

Search

Navigation