Abstract
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.
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Chung, M.K., Nacewicz, B.M., Wang, S., Dalton, K.M., Pollak, S., Davidson, R.J. (2008). Amygdala Surface Modeling with Weighted Spherical Harmonics . In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_20
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DOI: https://doi.org/10.1007/978-3-540-79982-5_20
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