TY - GEN
T1 - Statistical shape analysis for population studies via level-set based shape morphing
AU - Riklin Raviv, Tammy
AU - Gao, Yi
AU - Levitt, James J.
AU - Bouix, Sylvain
N1 - Funding Information:
This work was supported by the NIH grant R01 MH82918.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - We present a method that allows the detection, localization and quantification of statistically significant morphological differences in complex brain structures between populations. This is accomplished by a novel level-set framework for shape morphing and a multi-shape dissimilarity-measure derived by a modified version of the Hausdorff distance. The proposed method does not require explicit one-to-one point correspondences and is fast, robust and easy to implement regardless of the topological complexity of the anatomical surface under study. The proposed model has been applied to different populations using a variety of brain structures including left and right striatum, caudate, amygdala-hippocampal complex and superior- temporal gyrus (STG) in normal controls and patients. The synthetic databases allow quantitative evaluations of the proposed algorithm while the results obtained for the real clinical data are in line with published findings on gray matter reduction in the tested cortical and sub-cortical structures in schizophrenia patients.
AB - We present a method that allows the detection, localization and quantification of statistically significant morphological differences in complex brain structures between populations. This is accomplished by a novel level-set framework for shape morphing and a multi-shape dissimilarity-measure derived by a modified version of the Hausdorff distance. The proposed method does not require explicit one-to-one point correspondences and is fast, robust and easy to implement regardless of the topological complexity of the anatomical surface under study. The proposed model has been applied to different populations using a variety of brain structures including left and right striatum, caudate, amygdala-hippocampal complex and superior- temporal gyrus (STG) in normal controls and patients. The synthetic databases allow quantitative evaluations of the proposed algorithm while the results obtained for the real clinical data are in line with published findings on gray matter reduction in the tested cortical and sub-cortical structures in schizophrenia patients.
UR - http://www.scopus.com/inward/record.url?scp=84867733504&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33863-2_5
DO - 10.1007/978-3-642-33863-2_5
M3 - Conference contribution
AN - SCOPUS:84867733504
SN - 9783642338625
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 42
EP - 51
BT - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PB - Springer Verlag
T2 - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Y2 - 7 October 2012 through 13 October 2012
ER -