Reliability of Breast Density Estimation in Follow-Up Mammograms: Repeatability and Reproducibility of a Fully Automated Areal Percent Density Method

  • Youngwoo Kim
  • Jong Hyo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8539)


The aim of this study is to evaluate the reliability of the mammographic density estimations in follow-up examinations as measured by using a fully automated density estimation tool in terms of reproducibility and temporal stability. In our previous study, we have developed the fully automated mammographic density estimation method named as SIGMAM, which is based on the prior statistics of mammograms integrated into a novel level set scheme driven by a population-based tissue probability map (PTPM). This scheme was designed to capture the implicit knowledge of experts’ visual systems in which the learned knowledge was modeled as the PTPM, which was shown to provide relatively high correlation coefficient of 0.93 with experts’ estimations in a single equipment study (Senographe 2000D, GE). In this study, we evaluate the reliability of our SIGMAM method in follow-up mammogram examinations with respect to temporal stability and reproducibility. For evaluation of temporal stability, we selected 170 pairs of CC-view mammograms of 170 female patients taken with the same equipment (Senographe 2000D, GE) within one year from the breast cancer screening database in our institute. On the other hand, we collected pairs of mammograms taken with switched equipment: switched from GE (Senographe DS or Essential) to Hologic (Selenia). In total, 53 pairs of CC-view mammograms from 38 female patients taken within one or two months regarding the menstrual cycle were established as a dataset for reproducibility validation. The correlation coefficient of density estimates in temporal stability mammograms was 0.92, while that of the reproducibility mammograms was 0.87. In conclusion, our SIGMAM method showed relatively high reliability in both reproducibility and temporal stability.


CAD level set prior knowledge density estimation evaluation 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Youngwoo Kim
    • 1
  • Jong Hyo Kim
    • 2
  1. 1.Interdisciplinary Program of Radiation Applied Life ScienceSeoul National University College of MedicineSeoulRepublic of Korea
  2. 2.Department of Transdisciplinary StudiesSeoul National UniversitySuwonRepublic of Korea

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