Highlights
- •Clusters of microcalcifications can be considered as early signs of breast cancer.
- •A three phases approach is proposed: preprocessing-detection-clustering.
- •Application of circular Hough Transform for microcalcification detection.
- •The proposed method reached a sensitivity of 91.78
Abstract
Background
Microcalcification clusters in mammograms can be considered as early signs of breast
cancer. However, their detection is a very challenging task because of different factors:
large variety of breast composition, highly textured breast anatomy, impalpable size
of microcalcifications in some cases, as well as inherent low contrast of mammograms.
Thus, the need to support the clinicians’ work with an automatic tool.
Methods
In this work a three-phases approach for clustered microcalcification detection is
presented. Specifically, it is made up of a pre-processing step, aimed at highlighting
potentially interesting breast structures, followed by a single microcalcification
detection step, based on Hough transform, that is able to grasp the innate characteristic
shape of the structures of interest. Finally, a cluster identification step to group
microcalcifications is carried out by means of a clustering algorithm able to codify
expert domain rules.
Results
The detection performance of the proposed method has been evaluated on 364 mammograms
of 182 patients obtaining a true positive ratio of with false positives per image.
Conclusions
Experimental results demonstrated that the proposed method is able to detect microcalcification
clusters in digital mammograms showing performance comparable to different methodologies
exploited in the state-of-art approaches, with the advantage that it does not require
any training phase and a large set of data. The performance of the proposed approach
remains high even for more difficult clinical cases of mammograms of young women having
high-density breast tissue thus resulting in a reduced contrast between microcalcifications
and surrounding dense tissues.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Physica Medica: European Journal of Medical PhysicsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
National Cancer Institute. Cancer stat facts – female breast cancer. URL:https://seer.cancer.gov/statfacts/html/breast.html.
- Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA: Cancer J. 2018; 68: 394-424
- Cancer treatment and survivorship statistics.CA: Cancer J. 2016; 66: 271-289
- The emerging breast cancer epidemic: early diagnosis and treatment.Breast Cancer Res. 2010; 12: S10
- Annual report to the nation on the status of cancer, part i: National cancer statistics.Cancer. 2018; 124: 2785-2800
Breast cancer surveillance consortium. sensitivity, specificity, and false negative rate for 1,682,504 screening mammography examinations from 2007–2013. Based on bcsc data through 2013.https://www.bcsc-research.org/statistics/screening-performance-benchmarks/screening-sens-spec-false-negative.html; 2013.
- Radiology review manual.Lippincott Williams & Wilkins, 2011
- The relationship of mammographic density and age: implications for breast cancer screening.Am J Roentgenol. 2012; 198: W292-W295
- Breast tissue composition and susceptibility to breast cancer.J Natl Cancer Inst. 2010; 102: 1224-1237
- If you don’t find it often, you often don’t find it: Why some cancers are missed in breast cancer screening.PLoS One. 2013; 8e64366
- The importance of early detection of calcifications associated with breast cancer in screening.Breast Cancer Res Treat. 2018; 167: 451-458
- The efficacy of using computer-aided detection (cad) for detection of breast cancer in mammography screening: a systematic review.Acta Radiol. 2019; 60: 13-18
Ibm. Dream challenge results: Can machine learning help improve accuracy in breast cancer screening? URL:https://www.ibm.com/blogs/research/2017/06/dream-challenge-results/; 2017.
- Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.Med Phys. 2016; 43: 159-170
- A novel cascade classifier for automatic microcalcification detection.PLoS One. 2015; 10e0143725
- A two-stage method for microcalcification cluster segmentation in mammography by deformable models.Med Phys. 2015; 42: 5848-5861
- A hybrid image filtering method for computer-aided detection of microcalcification clusters in mammograms.J Med Eng. 2013;
- Fuzzy technique for microcalcifications clustering in digital mammograms.BMC Med Imag. 2014; 14: 23
- Automatic microcalcification and cluster detection for digital and digitised mammograms.Knowl-Based Syst. 2012; 28: 68-75
- Independent component analysis to detect clustered microcalcification breast cancers.Scientific World J. 2012;
Wang J, Nishikawa R, Yang Y. Global detection approach for clustered microcalcifications in mammograms using a deep learning network. J Med Imag 4 (2).
- Discovering mammography-based machine learning classifiers for breast cancer diagnosis.J Med Syst. 2012; 36: 2259-2269
- Benchmarking datasets for breast cancer computer-aided diagnosis (CADx).in: Ruiz-Shulcloper J. Sanniti di Baja G. Progress in pattern recognition, image analysis, computer vision, and applications. Springer, Berlin Heidelberg, Berlin, Heidelberg2013: 326-333
- Hough transform for clustered microcalcifications detection in full-field digital mammograms.in: Applications of digital image processing XL, vol. 10396. International Society for Optics and Photonics, 2017: 1039616
- 2013 ACR BI-RADS atlas: breast imaging reporting and data system.American College of Radiology, 2014
- Biomedical image processing.Computer. 1983; 16: 22-34
- Digital image processing.3rd ed. Prentice-Hall Inc, Upper Saddle River, NJ, USA2006
- Machine analysis of bubble chamber pictures.Conf Proc. 1959; 590914: 554-558
- On the Hough technique for curve detection.IEEE Trans Comput. 1978; 27: 923-926
- Circular hough transform.Aalborg University, Vision, Graphics, and Interactive Systems, 2007
- Breast microcalcifications: the lesions in anatomical pathology.Diagnostic Interven Imag. 2014; 95: 141-152
- Microcalcification segmentation from mammograms: a morphological approach.J Digital Imag. 2017; 30: 172-184https://doi.org/10.1007/s10278-016-9923-8
- Breast calcifications: mammographic evaluation.Radiology. 1986; 160: 289-293
- Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: a mathematical modeling approach.J Digital Imag. 2011; 24: 764
- Classification.2nd ed.Chapman & Hall/CRC Monographs on Statistics & Applied Probability. CRC Press, 1999
- The digital database for screening mammography.in: Yaffe M. Proceedings of the fifth international workshop on digital mammography. Medical Physics Publishing, 2001: 212-218
Wang J, Yang X, Cai H, Tan W, Jin C, Li L. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Scientific Rep 6-2016.
- Large scale deep learning for computer aided detection of mammographic lesions.Med Image Anal. 2017; 35: 303-312
- Integration of 3d digital mammography with tomosynthesis for population breast-cancer screening (storm): a prospective comparison study.Lancet Oncol. 2013; 14: 583-589
- Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening.Radiology. 2013; 269: 694-700
- Prospective trial comparing full-field digital mammography (ffdm) versus combined ffdm and tomosynthesis in a population-based screening programme using independent double reading with arbitration.Eur Radiol. 2013; 23: 2061-2071
- Detection and classification of calcifications on digital breast tomosynthesis and 2d digital mammography: a comparison.Am J Roentgenol. 2011; 196: 320-324
- Calcifications in the breast and digital breast tomosynthesis.Breast J. 2011; 17: 638-644
- Characterisation of microcalcification clusters on 2d digital mammography (ffdm) and digital breast tomosynthesis (dbt): does dbt underestimate microcalcification clusters? results of a multicentre study.Eur Radiol. 2015; 25: 9-14
Article info
Publication history
Published online: June 14, 2019
Accepted:
May 25,
2019
Received in revised form:
May 8,
2019
Received:
February 14,
2019
Identification
Copyright
© 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
