A dual-stage method for lesion segmentation on digital mammograms.
Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures. In this article, we present a method for automatic delineation of lesion boundaries on digital mammograms. This method utilizes a geometric active contour model that minimizes an energy function based on the homogeneities inside and outside of the evolving contour. Prior to the application of the active contour model, a radial gradient index (RGI)-based segmentation method is applied to yield an initial contour closer to the lesion boundary location in a computationally efficient manner. Based on the initial segmentation, an automatic background estimation method is applied to identify the effective circumstance of the lesion, and a dynamic stopping criterion is implemented to terminate the contour evolution when it reaches the lesion boundary. By using a full-field digital mammography database with 739 images, we quantitatively compare the proposed algorithm with a conventional region-growing method and an RGI-based algorithm by use of the area overlap ratio between computer segmentation and manual segmentation by an expert radiologist. At an overlap threshold of 0.4, 85% of the images are correctly segmented with the proposed method, while only 69% and 73% of the images are correctly delineated by our previous developed region-growing and RGI methods, respectively. This resulting improvement in segmentation is statistically significant.
Yuan
Yading
Y
Department of Radiology, Committee on Medical Physics, The University of Chicago, 5841 South Maryland Avenue-MC 2026, Chicago, Illinois 60637, USA. yading@uchicago.edu
Giger
Maryellen L
ML
Li
Hui
H
Suzuki
Kenji
K
Sennett
Charlene
C
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation* / Q000379:methods*; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results