CNF:Weighted Mesh To Image

Repository

1. https://git-community.cs.odu.edu/crtc/Utilities/weightedmeshtoimage

Code

Summer 2020

Create a new image using the weights of:

1. k-nearest-neighbors points as values in a weighted-mean, were weight[i] = 1 / distance(pixel-point, neighbor-point[i]) ^ power.
2. cell points as values in a weighted-mean, were weight[i] = 1 / distance(pixel-point, cell-point[i]) ^ power.
   1. Note: If the pixel point is not inside a cell, then the algorithm `1` will be used.
3. the intersection set of k-nearest-neighbors points and cell points as values in a weighted-mean, were weight[i] = 1 / distance(pixel-point, intersection-point[i]) ^ power.
   1. Note 1: If the Intersection set is empty, then the algorithm `2` will be used.
   2. Note 2: If the pixel point is not inside a cell, then the algorithm `1` will be used.
4. cell points as values in a weighted-mean, were weight[i] = probe-value[i] based on the location of each pixel point inside a cell. Probing values are result of FindCell function.
   1. Note: If the pixel point is not inside a cell, then the algorithm `1` will be used.

Comparison Metrics:

1. Mean Weighted pixel value difference
   1. 
2. Average Hausdorff Distance (itk filter)
3. Similarity Index (itk filter)

Examples

Summer 2020

1. Input Image
2. Input Mesh

1. KNN, number of points = 4, power = 1.5

1. 1. Mean Weighted pixel value difference: 0.000395461
   2. Average Hausdorff Distance: 0.030885
   3. Similarity Index: 0.986463

1. 1. Mean Weighted pixel value difference: 0.0002258
   2. Average Hausdorff Distance: 0.030885
   3. Similarity Index: 0.986463

1. 1. Mean Weighted pixel value difference: 0.000273839
   2. Average Hausdorff Distance: 0.030885
   3. Similarity Index: 0.986463

1. 1. Mean Weighted pixel value difference: 9.17551e-05
   2. Average Hausdorff Distance: 0.030885
   3. Similarity Index: 0.986463