Discrete dynamic contour model for mass segmentation in digital mammograms

In recent years, deformable models have become popular in the field of medical image analysis. We have applied a member of this family, a discrete dynamic contour model, to the task of mass segmentation in digital mammograms. The method was compared to a recently published region growing method on a dataset of 214 mammograms. Both methods need a starting point. In a first experiment, for each mass the center of gravity of the annotation was used. In a second experiment, a pixel-based initial detection step was used to generate starting points. The latter starting points are often located less proper for good segmentation, requiring the methods to be robust. The performance was measured using an overlap criterion based on the annotation made by an experienced radiologist and the segmented region. The discrete contour model proved to be a robust method to segment masses, and outperformed a probabilistic region growing method. However, just like for the region growing methods, a good choice for the seed point appeared to be of great importance.