S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Radiation Applied Life Science (대학원 협동과정 방사선응용생명과학전공) Theses (Ph.D. / Sc.D._협동과정 방사선응용생명과학전공)
A Population-Based Tissue Probability Map-Driven Level Set Method for Fully Automated Mammographic Density Estimations
유방 밀도 자동 측정을 위한 통계적 조직 확률 모델 기반의 Level Set 방법
- 의과대학 협동과정 방사선응용생명과학전공
- Issue Date
- 서울대학교 대학원
- prior statistics ; level set ; mammographic breast density ; quantitative measure ; full field digital mammography
- 학위논문 (박사)-- 서울대학교 대학원 : 협동과정 방사선응용생명과학전공, 2014. 8. 김종효.
- Introduction: A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, I present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations.
Methods: I modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts visual systems. The PTPM was constructed using an image database of a selected population consisting of 397 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour.
Results: A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47.
Conclusions: The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts visual systems and has the potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.