Heat Kernel Smoothing in Irregular Image Domains
This work addresses the challenge of processing irregular image domains, such as lung vessel trees, for medical imaging applications, though it appears incremental as it adapts an existing smoothing method to a specific domain.
The authors tackled the problem of smoothing data in irregularly shaped 3D image domains by presenting a discrete version of heat kernel smoothing on graph data structures, deriving new statistical properties and applying it to filter data in lung blood vessel trees from computed tomography.
We present the discrete version of heat kernel smoothing on graph data structure. The method is used to smooth data in an irregularly shaped domains in 3D images. New statistical properties are derived. As an application, we show how to filter out data in the lung blood vessel trees obtained from computed tomography. The method can be further used in representing the complex vessel trees parametrically and extracting the skeleton representation of the trees.