Geodesic transformation of images can be implementated with two approaches: fast marching and raster scan. Fast marching is based on the iterative propagation of a pixel front with velocity F [1]. Raster scan is based on kernel operations that are sequentially applied over the image in multiple passes [2][3]. In GeoS [4], the authors proposed to use a 3x3 kernel for forward and backward passes for efficient geodesic distance transform, which was used for image segmentation.

Raster scan for geodesic distance transform. Image from [4].

DeepIGeoS [5] proposed to combine geodesic distance transforms with convolutional neural networks for efficient interactive segmentation of 2D and 3D images.

• [1] Sethian, James A. "Fast marching methods." SIAM review 41, no. 2 (1999): 199-235.
• [2] Borgefors, Gunilla. "Distance transformations in digital images." CVPR, 1986
• [3] Toivanen, Pekka J. "New geodesic distance transforms for gray-scale images." Pattern Recognition Letters 17, no. 5 (1996): 437-450.
• [4] Criminisi, Antonio, Toby Sharp, and Andrew Blake. "Geos: Geodesic image segmentation." ECCV, 2008.
• [5] Wang, Guotai, et al. "DeepIGeoS: A deep interactive geodesic framework for medical image segmentation." TPAMI, 2018. https://ieeexplore.ieee.org/document/8370732

A comparison of fast marching and ranster scan for 2D geodesic distance transform. (d) shows the Euclidean distance and (e) is a mixture of Geodesic and Euclidean distance.

This repository provides a cpp implementation of fast marching and raster scan for 2D/3D geodesic and Euclidean distance transforms and a mixture of them, and proivdes a python interface to use it. This is part of the work of DeepIGeoS [5]. If you use our code, please cite this paper. https://ieeexplore.ieee.org/document/8370732

1, build the cpp files with python, run python setup.py build

2, install the package to default python path, run python setup.py install

3, see a 2D example, run python test2d.py

4, see a 3D example, run python test3d.py