Multivariate images are now commonly produced in many applications. If their process is possible due to
computers power and new programming languages, theoretical difficulties have still to be solved. Standard
image analysis operators are defined for scalars rather than for vectors and their extension is not immediate.
Several solutions exist but their pertinence is hardly linked to context. In the present paper we are going to get
interested in segmentation of vector images also including a priori knowledge. The proposed strategy combines
a decision procedure (where points are classified) and an automatic segmentation scheme (where regions are
properly extracted). The classification is made using a Bayesian classifier. The segmentation is computed via
a region growing method: the morphological Watershed transform. A direct computation of the Watershed
transform on vector images is not possible since vector sets are not ordered. So, the Bayesian classifier is used
for computing a scalar distance map where regions are enhanced or attenuated depending on their similitude
to a reference shape: the current distance is the Mahalanobis distance. This combination allows to transfer
the decision function from pixels to regions and to preserve the advantages of the original Watershed transform
defined for scalar functions. The algorithm is applied for segmenting colour images (with a priori) and medical images, especially dermatology images where skin lesions have to be detected.