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
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