Breast Arterial Calcification Detection in Mammography

• Background

  
  

  Recently, it has been reported that Breast Arterial Calcification (BAC) is potentially associated with cardiovascular disease, bone mineral density reduction, diabetes, and hypertension. In most studies, the severity of BAC was evaluated subjectively. To corroborate that BAC can actually be an effective marker of women’s risk for cardiovascular diseases, more quantitative evidence should be gathered. Accordingly, effective means of quantification and assessment of BAC severity are extremely crucial for hypothesis validation and the systematic study of the vast amount of data. In this study, an automatic BAC detection algorithm is developed for quantitative measurement of BAC in mammograms

• Method and Materials

  
  The proposed algorithm is implemented in two major steps: a random-walk based tracking step and a compiling-and-linking step. With given seeds from detected calcification points, the tracking algorithm traverses a vesselness map by exploring the uncertainties of three tracking factors, i.e., traversing direction, jumping distance, and vesselness value, to generate all possible sampling paths. Based on a random-walk mechanism, the problems of vessel branching and overlapping can be potentially addressed. The compiling-and-linking algorithm further organizes and groups all sampling paths into calcified vessel tracts.

• Demo

  

   

Reference:

1. "Detection of Arterial Calcification in Mammograms by Random Walks", IPMI 2009, Williamsburg, VA, July 5-10, 2009. [Jie-Zhi Cheng, Elodia Cole, Etta D. Pisano, Dinggang Shen]