Significant cardiac and respiratory motion of the living subject, occasional spells of defocus, drifts in the field of view,
and long image sequences make the registration of in-vivo microscopy image sequences used in atherosclerosis study an
onerous task. In this study we developed and implemented a novel Minimum Spanning Tree (MST)-based clustering
method for image sequence registration that first constructs a minimum spanning tree for the input image sequence. The
spanning tree re-orders the images in such a way where poor quality images appear at the end of the sequence. Then the
spanning tree is clustered into several groups based on the similarity of the images. Subsequently deformable registration
is conducted locally within the group with respect to the local anchor image selected automatically from the images
in the group. After that coarse registration is performed to find the global anchor and then a deformable registration is
performed globally to incorporate larger drift and distortion. Two-stage deformable registration incrementally incorporates
larger drifts and distortions present in the longer sequence. Our algorithm involves very few tuning parameters, the optimal
value of these parameters can be easily learned from data. Our method outperforms other methods on microscopy
image sequences of mouse arteries.