Detection of arterial calcification in mammograms by random walks.
A fully automatic algorithm is developed for breast arterial calcification extraction in mammograms. This 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 the 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. The compiling and linking algorithm further organizes and groups all sampling paths into calcified vessel tracts. The experimental results show that the performance of the proposed automatic calcification extraction algorithm is statistically close to that obtained by manual delineations.
Cheng
Jie-Zhi
JZ
Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 106 Mason Farm Road, Chapel Hill, NC 27599, USA. jzcheng@med.unc.edu
Cole
Elodia B
EB
Pisano
Etta D
ED
Shen
Dinggang
D
D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001185:Artificial Intelligence; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D016491:Peripheral Vascular Diseases / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*