Segmentation via morphological granulometric features
is based on fitting structuring elements into image topography from
below and above. Each structuring element captures a specific texture
content. This paper applies granulometric segmentation to digitized
mammograms in an unsupervised framework. Granulometries
based on a number of flat and nonflat structuring elements are computed,
local size distributions are tabulated at each pixel,
granulometric-moment features are derived from these size distributions
to produce a feature vector at each pixel, the Karhunen–Loeve
transform is applied for feature reduction, and Voronoi-based clustering
is performed on the reduced Karhunen–Loeve feature set.
Various algorithmic choices are considered, including window size
and shape, number of clusters, and type of structuring elements.
The algorithm is applied using only granulometric texture features,
using gray-scale intensity along with the texture features, and on a
compressed mammogram. Segmentation results are clinically
evaluated to determine the algorithm structure that best accords to
an expert radiologist’s view of a set of mammograms.