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A dual‐stage method for lesion segmentation on digital mammograms
Author(s) -
Yuan Yading,
Giger Maryellen L.,
Li Hui,
Suzuki Kenji,
Sennett Charlene
Publication year - 2007
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2790837
Subject(s) - segmentation , artificial intelligence , active contour model , computer science , computer vision , digital mammography , image segmentation , boundary (topology) , pattern recognition (psychology) , mammography , scale space segmentation , mathematics , medicine , mathematical analysis , cancer , breast cancer
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.