Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans

  • Woo Kyung Moon
  • Chung-Ming Lo
  • Jin Mo Goo
  • Min Sun Bae
  • Jung Min Chang
  • Chiun-Sheng Huang
  • Jeon-Hor Chen
  • Violeta Ivanova
  • Ruey-Feng Chang
Research Article


A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %–8.52 %) and 3.09 % (0.20 %–6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r = 0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.


Breast density CT Mammography Fuzzy c-mean Image registration 



The authors thank the National Science Council (NSC 99-2221-E-002-136-MY3), Ministry of Economic Affairs (100-EC-17-A-19-S1-164) of the Republic of China and National Taiwan University (101R890863) for the funding support. This work was supported by the Industrial Strategic Technology Development Program (10042581) funded by the Ministry of Knowledge Economy (MKE, Korea) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-01010846).

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Woo Kyung Moon
    • 1
  • Chung-Ming Lo
    • 2
  • Jin Mo Goo
    • 1
  • Min Sun Bae
    • 1
  • Jung Min Chang
    • 1
  • Chiun-Sheng Huang
    • 3
  • Jeon-Hor Chen
    • 4
    • 5
  • Violeta Ivanova
    • 2
  • Ruey-Feng Chang
    • 2
    • 6
  1. 1.Department of RadiologySeoul National University HospitalSeoulSouth Korea
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiRepublic of China
  3. 3.Department of SurgeryNational Taiwan University Hospital and National Taiwan University College of MedicineTaipeiTaiwan
  4. 4.Center for Functional Onco-Imaging and Department of Radiological ScienceUniversity of California IrvineIrvineUSA
  5. 5.Department of RadiologyE-Da Hospital and I-Shou UniversityKaohsiungTaiwan
  6. 6.Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan

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