Collections > UNC Chapel Hill Undergraduate Honors Theses Collection > A Framework for the Statistical Shape Analysis using SPHARM-PDM combined with ITK Conformal Flattening Filter
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Shape analysis is an important and powerful method used in neuroimaging research community due to its potential to precisely locate morphological changes between healthy and pathological structures. A popular shape analysis in the neuroimaging community is based on the encoding surface locations as spherical harmonics for a representation called SPHARM–PDM. The SPHARM-PDM pipeline takes a set of brain segmentation of a single brain structure (for example, hippocampus or caudate nucleus) as input and converts them into a corresponding spherical harmonic description (SPHARM), which is then sampled into triangulated surface (SPHARM-PDM). At present, the SPHARM-PDM pipeline utilizes an area-preserving optimization of the spherical mapping based on an initial heat-equation based mapping of the surface mesh to the unit sphere. In the case of objects with complex shape, this initial mapping will suffer from a high degree of mapping distortion that cannot always be corrected by the following optimization procedure. Here we proposed the use of an alternative initialization based on the ITK Conformal Flattening filter. This method adopts a bijective angle preserving conformal flattening scheme to replace the heat equation mapping scheme as initialization for use in the SPHARM-PDM pipeline. After quantitative measures of shape calculated from various complex structures, we concluded that in most cases, the new pipeline produced dramatically better results than the old pipeline.