  We present in this paper an image segmentation approach that combines a fuzzy
semantic region classification and a context based region-growing. Input image
is first over-segmented. Then, prior domain knowledge is used to perform a
fuzzy classification of these regions to provide a fuzzy semantic labeling.
This allows the proposed approach to operate at high level instead of using
low-level features and consequently to remedy to the problem of the semantic
gap. Each over-segmented region is represented by a vector giving its
corresponding membership degrees to the different thematic labels and the whole
image is therefore represented by a Regions Partition Matrix. The segmentation
is achieved on this matrix instead of the image pixels through two main phases:
focusing and propagation. The focusing aims at selecting seeds regions from
which information propagation will be performed. Thepropagation phase allows to
spread toward others regions and using fuzzy contextual information the needed
knowledge ensuring the semantic segmentation. An application of the proposed
approach on mammograms shows promising results
