scikit-mpe 0.2.3

Minimal path extraction using the fast marching method

scikit-mpe

scikit-mpe is a package for extracting a minimal path in N-dimensional Euclidean space (on regular Cartesian grids) using the fast marching method.

Quickstart

Installing

pip install -U scikit-mpe

Usage

Here is a simple example that demonstrates how to extract the path passing through the retina vessels.

from matplotlib import pyplot as plt

from skimage.data import retina
from skimage.color import rgb2gray
from skimage.transform import rescale
from skimage.filters import sato

from skmpe import mpe

image = rescale(rgb2gray(retina()), 0.5)
speed_image = sato(image)

start_point = (76, 388)
end_point = (611, 442)
way_points = [(330, 98), (554, 203)]

path_info = mpe(speed_image, start_point, end_point, way_points)

px, py = path_info.path[:, 1], path_info.path[:, 0]

plt.imshow(image, cmap='gray')
plt.plot(px, py, '-r')

plt.plot(*start_point[::-1], 'oy')
plt.plot(*end_point[::-1], 'og')
for p in way_points:
    plt.plot(*p[::-1], 'ob')

plt.show()

Documentation

The full documentation can be found at scikit-mpe.readthedocs.io

References

• Fast Marching Methods: A boundary value formulation
• Level Set Methods and Fast Marching Methods
• scikit-fmm - Python implementation of the fast marching method
• ITKMinimalPathExtraction - ITK based C++ implementation of MPE

License

MIT