Accurate, robust, and efficient automated segmentation of anatomical structures is difficult because of their complexity and inter-patient and inter-acquisition variations in shape, appearance, and spatial arrangement. To deal with the complexity and variations of anatomical structures, a number of model-based segmentation methods (e.g. snakes, shape and appearance models, image templates, etc.) have been proposed in the past. However, because applying individual models often does not lead to robust and accurate segmentation, we propose a framework for image segmentation by connected parametrical models. In our framework, which is a generalization of Fischler and Elschlager's pictorial structure (IEEE TC, 1973) and Bernard's et al. hierarchical principal component analysis framework (MICCAI, 2001), models cooperate via their parameters at different levels of hierarchy. In parametrical space, defined by parameters of all cooperating models, parameters may be connected by physical or statistical models. Connections may work either between intrinsic parameters of individual models, such as position, orientation and scale, or alternatively between arbitrarily transformed parameters, obtained, for instance, by PCA. By selecting appropriate parametrical models and models of connections between them, segmentation by the proposed framework has the potential to achieve accurate and robust results on a large variety of medical images. A framework, incorporating physical and/or statistical models of connections between intrinsic or transformed parameters is demonstrated on real images.