System and method for alignment of volumetric and surface scan images
A method for alignment of volumetric and surface scan images, said method comprising the steps of: receiving a volumetric image and surface scan image, wherein the volumetric image is a three-dimensional voxel array of a maxillofacial anatomy of a patient and the surface scan image is a polygonal mesh corresponding to the maxillofacial anatomy of the same patient; segmenting the volumetric image and surface scan image into a set of distinct anatomical structures by assigning each voxel in the volumetric image an identifier by structure and assigning each vertex or face of the mesh from the surface scan image an identifier by structure, wherein at least one of the distinct anatomical structures are in common between the volumetric and the surface scan image; extracting a polygonal mesh from the volumetric image featuring common structures with the polygonal mesh from the surface scan image; converting both meshes from the volumetric image and from the surface scan to a point cloud; and aligning the converted meshes via point clouds using a point set registration.
1 . A method for alignment of volumetric and surface scan images, said method comprising the steps of:
receiving a volumetric image and surface scan image, wherein the volumetric image is a three-dimensional voxel array of a maxillofacial anatomy of a patient and the surface scan image is a polygonal mesh corresponding to the maxillofacial anatomy of the same patient;
segmenting the volumetric image and surface scan image into a set of distinct anatomical structures by assigning each voxel in the volumetric image an identifier by structure and assigning each vertex or face of the mesh from the surface scan image an identifier by structure, wherein at least one of the distinct anatomical structures are in common between the volumetric image and the surface scan image;
extracting a polygonal mesh from the set of distinct anatomical structures of the volumetric image having common anatomical structures with the polygonal mesh from the surface scan image;
converting the common anatomical structures from both the polygonal meshes into a point cloud; and
aligning the point cloud derived from the converted meshes.
2 . The method of claim 1 , wherein the volumetric assignment is by finding a minimal bounding rectangle around the voxels belonging to a localized anatomical structure (discrete).
3 . The method of claim 1 , wherein the volumetric assignment is by defining a probability distribution over anatomical classes based on an output of a neural network probabilistic distribution for each of the anatomical structures.
4 . The method of claim 1 , wherein the surface scan assignment is by assigning each vertex and/or face of the mesh a distinct anatomical structure identifier.
5 . The method of claim 1 , wherein segmentation further comprises the step of assigning a voxel to a segmented tooth's dental crown if the distance between this voxel and the tooth's highest point is within a predefined threshold.
6 . The method of claim 5 , wherein the pre defined threshold of distance between the voxel and the tooth's highest point is not greater than 6 mm for the lower (upper) jaw tooth.
7 . The method of claim 1 , further comprising the step of normalizing the volumetric image intensity values by eliminating the values lying outside a standard range to derive zero mean and unit standard deviation.
8 . The method of claim 1 , further comprising the step of normalizing the surface scan image by centering and rescaling the mesh vertices to fit an unit sphere.
9 . The method of claim 1 , wherein the segmentation is at least one of discrete and/or probabilistic.
10 . The method of claim 1 , wherein selection of points on the surface of anatomical structures of volumetric image is by applying a binary erosion on the voxels corresponding to a structure (eroded mask) and then subtracting the eroded mask from a non-eroded mask revealing voxels on the boundary for selection.
11 . The method of claim 1 , wherein selection of points on the surface of anatomical structures of volumetric image is done by convolving a binary segmentation image with an edge-detection convolution kernel.
12 . The method of claim 10 , wherein the boundary points are taken as selecting a subset of boundary voxels as a point set by selecting a random subset of points to keep a number of points similar to a number of points on a corresponding structure in a polygonal mesh.