TY - GEN
T1 - Inter-point procrustes
T2 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
AU - Lekadir, Karim
AU - Frangi, Alejandro F.
AU - Yang, Guang Zhong
N1 - Funding Information:
Acknowledgment. This work was partly funded by the Spanish Ministry of Science and Innovation (Grant TIN2009-14536-C02-01) and partly by the U.K. Engineering and Physical Sciences Research Council (Grant GR/T06735/0). Karim Lekadir was supported by a Juan de la Cierva fellowship from the Spanish Ministry of Science and Innovation.
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - This paper presents a new approach for the robust alignment and interpretation of 3D anatomical structures with large and localized shape differences. In such situations, existing techniques based on the well-known Procrustes analysis can be significantly affected due to the introduced non-Gaussian distribution of the residuals. In the proposed technique, influential points that induce large dissimilarities are identified and displaced with the aim to obtain an intermediate template with an improved distribution of the residuals. The key element of the algorithm is the use of pose invariant shape variables to robustly guide both the influential point detection and displacement steps. The intermediate template is then used as the basis for the estimation of the final pose parameters between the source and destination shapes, enabling to effectively highlight the regional differences of interest. The validation using synthetic and real datasets of different morphologies demonstrates robustness up-to 50% regional differences and potential for shape classification.
AB - This paper presents a new approach for the robust alignment and interpretation of 3D anatomical structures with large and localized shape differences. In such situations, existing techniques based on the well-known Procrustes analysis can be significantly affected due to the introduced non-Gaussian distribution of the residuals. In the proposed technique, influential points that induce large dissimilarities are identified and displaced with the aim to obtain an intermediate template with an improved distribution of the residuals. The key element of the algorithm is the use of pose invariant shape variables to robustly guide both the influential point detection and displacement steps. The intermediate template is then used as the basis for the estimation of the final pose parameters between the source and destination shapes, enabling to effectively highlight the regional differences of interest. The validation using synthetic and real datasets of different morphologies demonstrates robustness up-to 50% regional differences and potential for shape classification.
UR - http://www.scopus.com/inward/record.url?scp=84872926311&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33454-2_13
DO - 10.1007/978-3-642-33454-2_13
M3 - Conference contribution
C2 - 23286119
AN - SCOPUS:84872926311
SN - 9783642334535
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 106
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
A2 - Ayache, Nicholas
A2 - Delingette, Herve
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer-Verlag Italia
Y2 - 1 October 2012 through 5 October 2012
ER -