In this paper, we propose a segmentation algorithm for multi-component images. The technique is based on the combination of three principles: it is an interband approach, where the correlations between the different image components are exploited; it is a multi-resolution technique, that is applied in the wavelet domain; it
is a model based segmentation technique, that applies a multinormal model for the multi-component image, where model parameters are estimated using Maximum Likelihood principles. From this procedure, a regionmerging segmentation technique emerges, employing a generalized likelihood ratio test for the merging. The procedure is embedded into a larger segmentation framework for multi-component images. This framework contains anisotropic diffusion noise filtering, watershed-based segmentation and a multiscale region merging procedure. All techniques are multiscale procedures and work in the wavelet domain. Moreover, they all are multicomponent techniques, making use of the correlation in between the different image components. To demonstrate the proposed procedure, it is applied to a 3-band color image.