TY - JOUR
T1 - MISTICA
T2 - Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences
AU - Ray, Nilanjan
AU - McArdle, Sara
AU - Ley, Klaus
AU - Acton, Scott T.
N1 - Funding Information:
Manuscript received March 13, 2015; revised July 29, 2015; accepted September 3, 2015. Date of publication September 21, 2015; date of current version December 6, 2016. The work of N. Ray was supported by the NSERC discovery grant. N. Ray is with the Computing Science Department, University of Alberta, Edmonton, AB T6G 2R3, Canada (e-mail: nray1@ualberta.ca). S. McArdle is with the La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037 USA, and also with the Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093 USA (e-mail: smcardle@liai.org). K. Ley is with the La Jolla Institute for Allergy and Immunology, La Jolla, CA 92093 USA (e-mail: klaus@liai.org). S. T. Acton is with the Electrical and Computer Engineering and Biomedical Engineering Departments, University of Virginia, Charlottesville, VA 22904 USA (e-mail: acton@virginia.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JBHI.2015.2480712
Publisher Copyright:
© 2015 IEEE.
PY - 2016/11
Y1 - 2016/11
N2 - 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.
AB - 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.
KW - Image sequence alignment
KW - microscopy image registration
KW - minimum spanning trees (MST)
UR - http://www.scopus.com/inward/record.url?scp=85027680282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027680282&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2015.2480712
DO - 10.1109/JBHI.2015.2480712
M3 - Article
C2 - 26415193
AN - SCOPUS:85027680282
SN - 2168-2194
VL - 20
SP - 1575
EP - 1584
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 6
ER -