A dual-stage method for lesion segmentation on digital mammograms

Yading Yuan, Maryellen L. Giger, Hui Li, Kenji Suzuki, Charlene Sennett

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)4180-4193
Number of pages14
JournalMedical Physics
Volume34
Issue number11
DOIs
StatePublished - 2007
Externally publishedYes

Keywords

  • Breast cancer
  • Computer-aided diagnosis
  • Geometric active contour model
  • Mass lesion segmentation

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