TY - JOUR
T1 - Fast image registration by hierarchical soft correspondence detection
AU - Shen, Dinggang
N1 - Funding Information:
We have presented a new approach to significantly improve the speed of our previous HAMMER registration algorithm. This is achieved by using a hierarchical soft correspondence detection technique to replace the one-to-one correspondence detection strategy in HAMMER, thus the correspondence detection can be performed more robustly and the image warping can be conducted straightforwardly and fast. In particular, for each active point selected during the registration procedure, it is allowed initially to have multiple correspondences detected in the other image, and gradually forced to have few (or even one) correspondences detected in the other image. By using this hierarchical soft correspondence detection technique, we can achieve not only the robustness but also the accuracy of registration. In summary, the image registration is here formulated as a process of hierarchically detecting a soft correspondence for each active point selected during the registration procedure, and also a process of interpolating and regularizing the dense smooth deformation field in the entire image domain. The experimental results indicate that, compared to HAMMER, first the speed of the proposed registration algorithm has been significantly increased without sacrificing much for the registration performance, and second the longitudinal consistency has been considerably improved. In future work, we will extensively evaluate and optimize all the parameters in our proposed registration algorithm, by using more real and simulated datasets with ground-truth information provided by manual raters or computer simulations. The final registration algorithm will be released publicly, as we did for the HAMMER registration algorithm, http://www.nitrc.org/projects/hammer/ . About the author —DINGGANG SHEN received all of his degrees from Shanghai JiaoTong University. He is an Associate Professor in the Department of Radiology at UNC-Chapel Hill since April 2008. He was an assistant professor (tenure-track) in the Department of Radiology at University of Pennsylvania (Upenn), from July 2002 to March 2008, and a faculty member in Johns Hopkins University from Jan 2001 and June 2002. Dr. Shen is on the Editorial Board of Pattern Recognition, International Journal of Image and Graphics, and International Journal for Computation Vision and Biomechanics. He also served as a reviewer for numerous international journals and conferences, as well as NIH, NSF and other grant foundations. He has published over 180 articles in journals and proceedings of international conferences. His research interests include medical image analysis, pattern recognition, and computer vision.
PY - 2009/5
Y1 - 2009/5
N2 - A new approach, based on the hierarchical soft correspondence detection, has been presented for significantly improving the speed of our previous HAMMER image registration algorithm. Currently, HAMMER takes a relative long time, e.g., up to 80 min, to register two regular sized images using Linux machine (with 2.40 GHz CPU and 2-Gbyte memory). This is because the results of correspondence detection, used to guide the image warping, can be ambiguous in complex structures and thus the image warping has to be conservative and accordingly takes long time to complete. In this paper, a hierarchical soft correspondence detection technique has been employed to detect correspondences more robustly, thereby allowing the image warping to be completed straightforwardly and fast. By incorporating this hierarchical soft correspondence detection technique into the HAMMER registration framework, both the robustness and the accuracy of registration (in terms of low average registration error) can be achieved. Experimental results on real and simulated data show that the new registration algorithm, based on the hierarchical soft correspondence detection, can run nine times faster than HAMMER while keeping the similar registration accuracy.
AB - A new approach, based on the hierarchical soft correspondence detection, has been presented for significantly improving the speed of our previous HAMMER image registration algorithm. Currently, HAMMER takes a relative long time, e.g., up to 80 min, to register two regular sized images using Linux machine (with 2.40 GHz CPU and 2-Gbyte memory). This is because the results of correspondence detection, used to guide the image warping, can be ambiguous in complex structures and thus the image warping has to be conservative and accordingly takes long time to complete. In this paper, a hierarchical soft correspondence detection technique has been employed to detect correspondences more robustly, thereby allowing the image warping to be completed straightforwardly and fast. By incorporating this hierarchical soft correspondence detection technique into the HAMMER registration framework, both the robustness and the accuracy of registration (in terms of low average registration error) can be achieved. Experimental results on real and simulated data show that the new registration algorithm, based on the hierarchical soft correspondence detection, can run nine times faster than HAMMER while keeping the similar registration accuracy.
KW - Deformable registration
KW - Feature matching
KW - Local descriptor
KW - Non-rigid registration
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U2 - 10.1016/j.patcog.2008.08.032
DO - 10.1016/j.patcog.2008.08.032
M3 - Article
AN - SCOPUS:58249089306
VL - 42
SP - 954
EP - 961
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 5
ER -