Project: Heat kernel construction on cortical surface using geodesic distance
نویسندگان
چکیده
منابع مشابه
Cortical thickness analysis in autism with heat kernel smoothing.
We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their 3D Euclidean distance, has been widely used in 3D brain images to increase the signal-to-noise ratio. When the observations lie on a convoluted brain surface, however, it is more natural ...
متن کاملHeat Kernel Smoothing and its Application to Cortical Manifolds
In brain imaging analysis, there is a need for analyzing data collected on the cortical surface of the human brain. Gaussian kernel smoothing has been widely used in this area in conjunction with random field theory for analyzing data residing in Euclidean spaces. The Gaussian kernel is isotropic in Euclidian space so it assigns the same weights to observations equal distance apart. However, wh...
متن کاملHeat Kernel Smoothing and Statistical Inference on Manifolds
In computational neuroanatomy, there is need for analyzing data collected on the cortical surface of the human brain. Gaussian kernel smoothing has been widely used in this area in conjunction with random field theory for analyzing data residing in Euclidean spaces. The Gaussian kernel is isotropic in Euclidian space so it assigns the same weights to observations equal distance apart. However, ...
متن کاملUnified Statistical Approach to Cortical Thickness Analysis
This paper presents a unified image processing and analysis framework for cortical thickness in characterizing a clinical population. The emphasis is placed on the development of data smoothing and analysis framework. The human brain cortex is a highly convoluted surface. Due to the convoluted non-Euclidean surface geometry, data smoothing and analysis on the cortex are inherently difficult. Wh...
متن کاملA Geometry Preserving Kernel over Riemannian Manifolds
Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data to high dimensional feature space without considering the intrinsic geometry of data points. ...
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تاریخ انتشار 2004