Not a fix but part of the way to fixing #1733

I replaced the filter and added some new features including computing mean and median intensities of gray images.

Some attributes don't have an exact analog. I changed some headers to be general over 2D / 3D

New columns are

Label
NumberOfVoxels
PhysicalSize - area in 2D (mm^2) or volume in 3D (mm^3)
Perimeter - perimeter in 2D or surface area in 3D
Elongation - Note this differs from old implementation
Roundness - new
Flatness - new
Centroid_x - Note this differs from old implementation
Centroid_y
Centroid_z
BoundingBoxLower_x - reordered (open to changing this)
BoundingBoxLower_y
BoundingBoxLower_z
BoundingBoxUpper_x
BoundingBoxUpper_y
BoundingBoxUpper_z
MeanIntensity - Grayscale stats if intensity image provided
SigmaIntensity
MedianIntensity
MinIntensity
MaxIntensity

Eccentricity, Orientation have been removed as they don't exist in the new filter. Also, I didn't add the "WeightedCentroid" for gray images, I don't think that exists in LabelStatisticsImageFilter.

To check the centroid, I compared to SimpleITK:

#!/usr/bin/env python

import SimpleITK as sitk
import sys

def main():
    if len(sys.argv) < 2:
        print("Usage:", sys.argv[0], "<InputImage>")
        return

    # Read the image file
    image_path = sys.argv[1]
    image = sitk.ReadImage(image_path)

    # Convert to a binary image, assuming the image might not be perfectly binary
    binary_image = image > 0

    # Calculate the centroid using the LabelShapeStatisticsImageFilter
    label_shape_filter = sitk.LabelShapeStatisticsImageFilter()
    label_shape_filter.Execute(binary_image)

    # Assuming the label of interest is '1' (common for binary images)
    centroid = label_shape_filter.GetCentroid(1)
    print(f"Centroid of the binary object: {centroid}")

if __name__ == "__main__":
    main()

which agrees with my definition but note that it differs from the old filter and also what c3d reports (I am not sure what filter it uses)