title: MISTICA: Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences.
abstract: Registration of an in vivo microscopy image sequence is necessary in many significant studies, including studies of atherosclerosis in large arteries and the heart. Significant cardiac and respiratory motion of the living subject, occasional spells of focal plane changes, drift in the field of view, and long image sequences are the principal roadblocks. The first step in such a registration process is the removal of translational and rotational motion. Next, a deformable registration can be performed. The focus of our study here is to remove the translation and/or rigid body motion that we refer to here as coarse alignment. The existing techniques for coarse alignment are unable to accommodate long sequences often consisting of periods of poor quality images (as quantified by a suitable perceptual measure). Many existing methods require the user to select an anchor image to which other images are registered. We propose a novel method for coarse image sequence alignment based on minimum weighted spanning trees (MISTICA) that overcomes these difficulties. The principal idea behind MISTICA is to reorder the images in shorter sequences, to demote nonconforming or poor quality images in the registration process, and to mitigate the error propagation. The anchor image is selected automatically making MISTICA completely automated. MISTICA is computationally efficient. It has a single tuning parameter that determines graph width, which can also be eliminated by the way of additional computation. MISTICA outperforms existing alignment methods when applied to microscopy image sequences of mouse arteries.
authors.forename: Nilanjan
authors.initials: N
authors.lastname: Ray
authors.forename: Sara
authors.initials: S
authors.lastname: McArdle
authors.forename: Klaus
authors.initials: K
authors.lastname: Ley
authors.forename: Scott T
authors.initials: ST
authors.lastname: Acton
mesh_terms: D000465:Algorithms; D000818:Animals; D001158:Arteries / Q000000981:diagnostic imaging; D016208:Databases, Factual; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D051379:Mice; D008853:Microscopy / Q000379:methods*