Polygonal Path Image
Find low-cost polygonal paths through grayscale images and turn them into path voting maps. This Python/Cython package modernizes the implementation of the Polygonal Path Image (PPI) method by Paula Agregán Reboredo, Vincent Bismuth, and Laurent Najman.
The core returns a minimum-cost path from every pixel, constrained to one of four cardinal cones. Lower pixel intensities are cheaper. Paths are useful for enhancing dark curvilinear structures; voting highlights pixels visited by many paths.
Version 0.1.0 is an initial research release of the later four-cone student implementation. It fixes a path reconstruction error in the historical source and has deterministic correctness tests. Full-resolution checks on both supplied example images preserve historical costs and validate every reconstructed path. See the validation report and scientific scope for the differences from the MICCAI method and the limits of these checks, and the migration notes for API changes.
Install
Requires Python 3.10 or later and NumPy 1.26 or later (including NumPy 2).
python -m pip install polygonal-path-image
Prebuilt wheels cover CPython 3.10–3.14 on Linux x86-64, Windows x86-64, and macOS Intel/Apple Silicon. On these platforms, pip installs the compiled extension without a local C compiler. Wheels and the source archive are also attached to GitHub releases. See supported platforms and release details.
Installing from source also requires a C compiler: Xcode Command Line Tools on macOS, GCC/Clang on Linux, or Microsoft C++ Build Tools on Windows. Pip installs the Python build dependencies automatically in an isolated environment.
python -m pip install "git+https://github.com/lnajman/polygonal-path-image.git"
Or install a local checkout:
git clone https://github.com/lnajman/polygonal-path-image.git
cd polygonal-path-image
python -m pip install .
NumPy is the only required runtime dependency. Pillow and Matplotlib are optional, used by the example and visualization scripts.
Use
import numpy as np
from polygonal_path_image import compute_ppi, filter_tortuosity, voting
image = np.full((32, 48), 200, dtype=np.uint8)
image[16, :] = 10 # A dark horizontal structure.
costs, paths = compute_ppi(image, segment_length=2, nb_segments=5)
filtered_costs = filter_tortuosity(costs, paths, threshold=0.75)
votes, inverted_votes = voting(filtered_costs, paths)
# The start of this path is (16, 10); its subsequent endpoints are:
print(paths[16, 10])
print(costs[16, 10])
- Input: a nonempty 2D
numpy.ndarraywith dtypeuint8. Convert other image types explicitly, without silently truncating floating-point intensities. costs: an(H, W)float64 array;infmeans no path with the requested length fits.paths: an(H, W, nb_segments, 2)int64 array of endpoint(row, column)coordinates. The source pixel is implicit. Impossible paths contain only-1.- A segment advances
segment_lengthpixels along its cone's main axis. Costs exclude the source pixel, include every segment endpoint, and count joints once. - Computation performs no file I/O or plotting and does not modify inputs.
Additional functions are bresenham_line, orientation, and prune_paths.
See the API and algorithm conventions.
Reproducible example
python -m pip install '.[examples]'
python examples/synthetic_curve.py --output example-output
This generates its own synthetic image with a fixed random seed and saves a
comparison figure and NumPy arrays. No historical image files are required.
Use --input path/to/image.png to process a grayscale conversion of another image.
Development
python -m pip install -e '.[dev,examples]'
python -m pytest
ruff check .
python -m build
python -m twine check dist/*
Tests enumerate all paths on small grids independently of the dynamic-programming implementation. They check optimal costs, exact tie behavior, reconstructed path costs, boundary conditions, invalid inputs, voting, tortuosity, orientation, and pruning. CI builds a wheel from the source distribution and tests the installed wheel on Linux, macOS, and Windows. NumPy 1.26 runtime compatibility is tested separately from current NumPy 2.
Working memory grows with H * W * (segment_length + nb_segments); the path array
alone uses 16 * H * W * nb_segments bytes. Start with modest images and path
lengths. The pure Python postprocessing routines prioritize clarity and can cost
more time than the compiled core; pruning may compare many pairs of paths.
Authors and citation
Package and original implementation authors: Paula Agregán Reboredo, Vincent Bismuth, and Laurent Najman. Paula developed the work as a student; Vincent and Laurent were her advisors and contributed to the code. Maintainer: Laurent Najman. See AUTHORS.md, CITATION.cff, and research provenance.
Original method: Vincent Bismuth, Régis Vaillant, Hugues Talbot, and Laurent Najman, Curvilinear Structure Enhancement with the Polygonal Path Image - Application to Guide-Wire Segmentation in X-Ray Fluoroscopy. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, Part II, Lecture Notes in Computer Science 7511, pp. 9–16, Springer, 2012. DOI: 10.1007/978-3-642-33418-4_2.
The maintained code is distributed under the BSD-3-Clause license.
1 maintainer
Release files for polygonal-path-image 0.1.0
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