GLIA: Graph learning library for image analysis
I have released glia on Github.
What?
A C++ library for efficient hierarchical image segmentation. Please cite the following papers accordingly if you use the code:
How?
Use a modern compiler with C++11 support, e.g., GCC-4.8 or higher and Apple LLVM 6.
Dependencies:Instructions:
Who?
What?
A C++ library for efficient hierarchical image segmentation. Please cite the following papers accordingly if you use the code:
- T. Liu, C. Jones, M. Seyedhosseini, T. Tasdizen. A modular hierarchical approach to 3D electron microscopy image segmentation. Journal of Neuroscience Methods, 226, pp. 88--102, 2014.
- T. Liu, E. Jurrus, M. Seyedhosseini, T. Tasdizen. Watershed merge tree classification for electron microscopy image segmentation. ICPR 2012.
- T. Liu, M. Seyedhosseini, T. Tasdizen. Image segmentation using hierarchical merge tree. IEEE Transactions on Image Processing, 25, pp. 4596--4607, 2016.
- T. Liu, M. Zhang, M. Javanmardi, N. Ramesh, T. Tasdizen. SSHMT: Semi-supervised hierarchical merge tree for electron microscopy image segmentation. ECCV 2016.
How?
Use a modern compiler with C++11 support, e.g., GCC-4.8 or higher and Apple LLVM 6.
Dependencies:Instructions:
- Use '-DCMAKE_CXX_FLAGS=-std=c++11' for the first time ITK CMake configuration.
- Turn on 'ITKReview' module.
- Enable C++11 for Boost libraries.
- Turn on 'GLIA_MT' to use OpenMP parallelization.
- Work on 3D/2D images with 'GLIA_3D' turned on/off.
- Turn on 'GLIA_BUILD_{HMT,SSHMT,LINK3D,GADGET,ML_RF}' modules accordingly.
- The random forest classifier used in our code is based on work of Andy Liaw (https://cran.r-project.org/web/packages/randomForest/index.html). To use the related functionalities, please set 'RF_SRC_DIR' as the path to 'randomForest/src/' folder in their code.
Who?
