The aim of this paper is to present a general methodology based on multispectral mathematical morphology in order to segment multispectral images. The methods consists in computing a probability density function pdf of contours conditioned by a spectral classification. The pdf is conditioned through regionalized random balls markers thanks to a new algorithm. Therefore the pdf contains spatial and spectral information. Finally, the pdf is segmented by a watershed with seeds (i.e. markers) coming from the classification.Consequently, a complete method, based on a classification-driven stochastic watershed is introduced. This approach requires a unique and robust parameter: the number of classes which is the same for similar images.Moreover, an efficient way to select factor axes, of Factor Correspondence Analysis (FCA), based on signal to noise ratio on factor pixels is presented.