Image segmentation by clustering represents a classical use-case of unsupervised learning. A key aspect of this problem is that instances that are being clusters may have various types and thus requesting specific algorithms that implement particular distance functions and quality metrics. This paper presents an improved version of MTree clusterer that has been tested in the context of image segmentation in the same setup as a new recently k-MS algorithm. The redesigned MTree algorithms allows many levers for setup so that many configurations are available depending on the particularities of the tackeled problem. The experimental results are promising especially as compared with the ones from previous MTree version and also as compared with classical clustering algorithms or newly developed k-MS algorithm. Further improvements in terms of available algorithms for configuration and algorithmic efficiency of integrations may lead the way to a general purpose clusterer that may be used for processing various data types.