2008 | OriginalPaper | Buchkapitel
Amygdala Surface Modeling with Weighted Spherical Harmonics
verfasst von : Moo K. Chung, Brendon M. Nacewicz, Shubing Wang, Kim M. Dalton, Seth Pollak, Richard J. Davidson
Erschienen in: Medical Imaging and Augmented Reality
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Although there are numerous publications on amygdala volumetry, so far there has not been many studies on modeling local amygdala surface shape variations in a rigorous framework. This paper present a systematic framework for modeling local amygdala shape. Using a novel surface flattening technique, we obtain a smooth mapping from the amygdala surface to a sphere. Then taking the spherical coordinates as a reference frame, amygdala surfaces are parameterized as a weighted linear combination of smooth basis functions using the recently developed weighted spherical harmonic representation. This new representation is used for parameterizing, smoothing and nonlinearly registering a group of amygdala surfaces. The methodology has been applied in detecting abnormal local shape variations in 23 autistic subjects compared against 24 normal controls. We did not detect any statistically significant abnormal amygdala shape variations in autistic subjects. The complete amygdala surface modeling codes used in this study is available at
http://www.stat.wisc.edu/~mchung/research/amygdala
.