Selecting the optimal sequence for deformable registration of microscopy image sequences using two-stage MST-based clustering algorithm

We developed and implemented a novel two-stage Minimum Spanning Tree (MST)-based clustering method for deformable registration of microscopy image sequences. We first construct a MST for the input image sequence. MST mitigates the registration error propagation of time sequenced images by re-ordering the images in such a way where poor quality images appear at the end of the sequence. Then MST is clustered into several groups based on the similarity of the images. After that an optimal anchor image is selected automatically for each group through an iterative assessment of entropy and MSE based coarse registration error and the local deformable registration is performed within each group separately. Subsequently coarse registration is conducted to find the global anchor image selected among the whole time sequenced images and then a deformable registration is conducted on the whole sequence. Two-stage MST-based deformable registration algorithm can incorporate larger drifts and distortions more accurately than conventional one shot registration algorithm by fine-tuning the larger amount of deformation incrementally in a couple of stages. Our method outperforms other methods on both 2D and 3D in vivo microscopy image sequences of mouse arteries used in atherosclerosis study.