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.
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 toolkit 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.
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Install this toolkit easily by typing
pip install GeodisTK -
Alternatively, if you want to build from source files, download this package and run the following:
python setup.py build
python setup.py install-
See a 2D example, run
python demo2d.py -
See a 3D example, run
python demo3d.py