DocumentCode :
1825701
Title :
Minimum description length with local geometry
Author :
Styner, Martin ; Oguz, Ipek ; Heimann, Tobias ; Gerig, Guido
Author_Institution :
Depts. of Comput. Sci., Univ. of North Carolina, Chapel Hill, NC
fYear :
2008
fDate :
14-17 May 2008
Firstpage :
1283
Lastpage :
1286
Abstract :
Establishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can´t always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there´s no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used.
Keywords :
biomedical MRI; biomedical measurement; curvature measurement; geometry; image registration; information theory; medical computing; shape measurement; statistical analysis; DTI; Gaussian curvature metrics; anatomical structures; correspondence quality; fMRI; image registration; local curvature metrics; local geometric characteristics; local geometry information; mean curvature metrics; medical imaging; minimum description length method; principal curvatures; spherical harmonics correspondence; statistical shape analysis; Biological information theory; Biomedical imaging; Biomedical informatics; Computational geometry; Computer science; Harmonic analysis; Information geometry; Psychiatry; Scientific computing; Shape; Correspondence; Image Registration; Image Shape Analysis; Modeling; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-2002-5
Electronic_ISBN :
978-1-4244-2003-2
Type :
conf
DOI :
10.1109/ISBI.2008.4541238
Filename :
4541238
Link To Document :
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