3D Landmark detector using Kernel-weighted Equivariant Filters
- This package includes the implementation of the 3D Landmark detector using Kernel-weighted Equivariant Filters. It is part of the "Virtual Brain Explorer for Zebrafish".
It is distributed for non-commercial use under the GNU General Public License, v3.0.
- Current release (the first version) and test data:
The program has a general design. However, we have only tested it on our own specific application. We hope that this open source release can allow one to use/modify this detector for their own applications.
How to compile
- The package is developed and tested on Ubuntu 10.04 X86_64.
Built binaries can be found in "/x86_64-bin".
- The package is build on some other free software:
Blitz++ | HDF5 software | FFTW v3.2 | VLFeat v0.9.13 | GSL - GNU Scientific Library
To ease the dependency management, please install them from the "/EXT_lib" folder.
To use the model training part, a Matlab installation is currently required.
- How to compile:
0. Check the prerequisites in "/EXT_lib/Readme.txt"
1. run "install.sh" in "/EXT_lib"
2. run "install.sh" in "/LMB_lib"
3. go to "Landmark3D/build", (edit the paths if necessary) and run "make"
How to use
- /Matlab/demo.m : how to use the toolbox when you have the data in Matlab.
It is recommendable as our programs use HDF5 file format, which is well supported in Matlab.
- /small_test.sh : script to run with the test data given in "/rawTestData".
The test data includes two zebrafish which are very similar. One is purposely rotated to demonstrate the rotation-invariance of the method. The test data folder should be put into the package root path.
- The data management is based on HDF5 file. The HDF5 file is well supported in Matlab.
- The data hierarchy (groups and datasets) has a default setting as it is in ViBE-Z, but most of it is configurable through the command-line interface.
- The provided test data (at "/rawTestData") is an example of the input to this toolbox, in the ViBE-Z pipeline. The "raw" image is stored in dataset "/step4/fused/channel0".
- The real-world size is always recorded in micrometer. The image voxel size has to be stored in the attribute "element_size_um" attached to the image dataset.
- To use the training function, the ground-truth landmark positions can be provided as a text file for each training image, see "/rawTestData/fish1_landmark.txt" as an example. Otherwise, the program will try to read the landmark information from the hdf5 files. (Check the command-line parameters for more detail).
2D/3D Rotation-Invariant Detection using Equivariant Filters and Kernel Weighted Mapping
Kun Liu, Qing Wang, W. Driever and Olaf Ronneberger
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
ViBE-Z: A Framework for 3D Virtual Colocalization Analysis in Zebrafish Larval Brains
Olaf Ronneberger, Kun Liu, Meta Rath, Dominik Ruess, Thomas Mueller, Henrik Skibbe, Benjamin Drayer, Thorsten Schmidt, Alida Filippi, Roland Nitschke, Thomas Brox, Hans Burkhardt, Wolfgang Driever
Nature Methods, 2012.
- Other related work:
Henrik Skibbe, Marco Reisert, Thorsten Schmidt, Thomas Brox, Olaf Ronneberger, Hans Burkhardt
Fast Rotation Invariant 3D Feature Computation utilizing Efficient Local Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
M. Schlachter, M. Reisert, C. Herz, F. Schluermann, S. Lassmann, M. Werner, H. Burkhardt and O. Ronneberger
Harmonic Filters for 3D Multi-Channel Data: Rotation Invariant Detection of Mitoses in Colorectal Cancer
IEEE Transactions on Medical Imaging, 29(8): 1485--1495, 2010
Marco Reisert and Hans Burkhardt
Harmonic Filters for Generic Feature Detection in 3D (DOI)
DAGM-Symposium 2009: pages 131-140
Qing Wang, Olaf Ronneberger and Hans Burkhardt
Rotational Invariance Based on Fourier Analysis in Polar and Spherical Coordinates
IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9): 1715--1722, 2009
Marco Reisert and Hans Burkhardt.
Equivariant Holomorphic Filters for Contour Denoising and Rapid Object Detection (DOI)
in IEEE Transactions on Image Processing, volume 17, number 2, February 2008