Fourier Descritpor-Based Deformable Models for Segmentation of the Distal Femur in CT
Anatomical shapes present a unique problem in terms of accurate representation and medical image segmentation. Three-dimensional statistical shape models have been extensively researched as a means of autonomously segmenting and representing models. We present a segmentation method driven by a statistical shape model based on a priori shape information from manually segmented training image sets. Our model is comprised of a stack of two-dimensional Fourier descriptors computed from the perimeters of the segmented training image sets after a transformation into a canonical coordinate frame. Our segmentation process alternates between a local active contour process and a projection onto a global PCA basis of the statistical shape model. We apply our method to the segmentation of CT and MRI images of the distal femur and show quantitatively that it recovers bone shape more accurately from real imagery than a recently published method recovers bone shape from synthetically segmented imagery.
Keywordsautomatic 3D image segmentation Fourier shape descriptors principal components analysis statistical shape model active contours snakes
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