Abstract : In this paper, we propose a new technique for the estimation of contrast enhancement curves of Dynamic Contrast Enhanced sequences, which takes the most from the interdependence between this estimation problem and the registration problem raised by possible movements occuring in sequences. The technique solves the estimation and registration problems simultaneously in an iterative way. However, unlike previous techniques, a pixel classification scheme is included within the estimation so as to compute enhancement curves on pixel classes instead of single pixels. The classification scheme is designed using a descendant hierarchical approach. Due to this tree approach, the number of classes is set automatically and the whole technique is entirely unsupervised. Moreover, some specific prior information about the form of enhancement curves are included in the splitting and pruning steps of the classification scheme. Such an information ensures that created classes include pixels having homogeneous and relevant enhancement properties. The technique is applied to DET-CT scan sequences and evaluated using ground truth data. Results show that classifications are anatomically sound and that constrast enhancements are accurately estimated from sequences. Moreover, due to the classification scheme, the curve estimation is more robust to the sequence noise than pixel-based estimations and the computation time is significantly lower.