MRFSEG+GAMIXTURE is a collection of open-source software tools written in C implementing a flexible voxel classification framework. The framework is based on a novel genetic algorithm based finite mixture model (GAMIXTURE) and a standard 3-D Markov random field (MRF) based on the iterative conditional modes (ICM) algorithm (MRFSEG). The key novelty in the MRFSEG program is the application of the configuration file to control MRF parameters. The software package is to be extended with a novel inhomogeneous MRF algorithm allowing even more flexible spatial and intensity information modeling. At the moment the software supports Analyze 7.5 images.
An extension of the above package dealing better with intensity nonuniformities is also available (see J. Tohka , I.D. Dinov, D.W. Shattuck, and A.W. Toga. Brain MRI Tissue Classification Based on Local Markov Random Fields, Magnetic Resonance Imaging, 28(4): 557 - 573 , 2010.). Now, supports both Analyze and NIFTI-files. Note that this software contains all the functionalities of the MRFSEG package above. This is meant to be a flexible voxel classification framework that can be easily tuned for particular applications. If, however, you're interested in partial volume estimation in high quality T1-weighted brain MRI, I would suggest to try PVEMRI below.
pvemri2 [Download] : A matlab implementation of the partial volume estimation for brain MRI(Tohka, Zijedenbos, Evans, NeuroImage 2004). The function also implement "the incremental k-means" based preliminary segmentation (Manjon et al MRM 2008). I'll also urge you to see our recent paper on benefits of non-local means filtering to partial volume estimation (Manjon, Tohka Robles, NeuroImage 2010) and the related code from Jose Manjon's website.
Installation: You'll need to compile two c files into mex. Saying
mex c_icm_trans.c
mex c_tissue_fractions.c
works most of the time. If for some reason a pure Matlab implementation (no mex files) is needed, it is here: pvemri2.m [Download] .
The new fast algorithms are described in J. Tohka. FAST-PVE: Extremely Fast Markov Random Field Based Brain MRI Tissue Classification. SCIA 2013, Scandinavian Conference on Image Analysis, Finland, 2013, Lecture Notes in Computer Science vol 7944 pp. 266 - 276, 2013. [Preprint] .
pvemri [Download (old version; for compatibility reasons; this one is not recommended anymore)]: A matlab implementation of the partial volume estimation for brain MRI (Tohka, Zijedenbos, Evans, NeuroImage 2004). Contains "pure" matlab implementation of the algorithm (slow, pvemri.m) and a very fast mex-based implementation (pvemrimex.m), which is recommended. The package includes mex files for 32-bit Windows XP and 32-bit Linux OS. If you have some other system you'll need to compile the included c file into mex-file. Saying mex c_icm_trans.c works most of the time. Please note that the two versions differ in order the voxels are traversed in the ICM algorithm. This can cause slight differences in the segmentation results between the two versions of the algorithm, none of them being "more correct" than the other. The algorithms are still fully deterministic.
DoGEll software for ultrasound fetal head segmentation (for nonprofit purposes only, TUT limited license). This is a fast implementation (C with Matlab wrapper) of the winning method of the ISBI2012 competition. References:
A. Foi, M. Maggioni, A. Pepe, S. Rueda, J.A. Noble, A. T. Papageorghiou, and J. Tohka . Difference of Gaussians Revolved Along Elliptical Paths for Ultrasound Fetal Head Segmentation. Computerized Medical Imaging and Graphics , 38(8): 774 - 784, 2014 .[Software (Matlab)] [Preprint]
S. Rueda, S. Fathima, C.L. Knight, M. Yaqub, A. T. Papageorghiou, B. Rahmatullah, A. Foi, M. Maggioni, A. Pepe, J. Tohka , R.V. Stebbing, J.E. McManigle, A. Ciurte, X. Bresson, M. Bach Cuadra, C. Sun, G. V. Ponomarev, M. S. Gelfand, M. D. Kazanov, C.-W. Wang, H.-C. Chen, C.-W. Peng, C.-M. Hung, and J.A. Noble. Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge.IEEE Transactions on Medical Imaging 33(4):797 - 813, 2014
ADisc version 1.1[Download]: Adaptive Disconnection is an algorithm for segmentating a brain MRI into left and right cerebrum, left and right cerebellum, and brain stem. It does this without any stereotactic registration just needing partial volume estimates to work. These can be obtained through pvemri (see above or [Download]). This was a part of Lu Zhao's PhD work in my team.
This new implementation made available here is very fast requiring about 5 minutes per image in contrast to slow running times reported in L. Zhao, U. Ruotsalainen, J. Hirvonen, J. Hietala and J. Tohka . Automatic cerebral and cerebellar hemisphere segmentation in 3D MRI: adaptive disconnection algorithm. Medical Image Analysis , 14(3): 360 - 372, 2010. The algorithm itself is the same one.
Version history version 1.0 (ADisc.zip): the original version; version 1.1 (current, version, ADisc11.zip): Minor bugs in function expand_tfe.m and shrink_tfe.m corrected.
g-ACSON software to visualize, process, and analyze three-dimensional electron microscopy images. The main developer is Andrea Behanova in my group (This was Andrea's M.Sc.). g-ACSON offers Graphical user interface (GUI) for the segmentation and quantification of EM images in grey matter in MATLAB, enabling user interactive segmentation, proofreading, and correcting errors of an automated segmentation.
A GUI-based segmentation tool for MiniTEM images. The main developer has been Andrea Behanova.
A MATLAB pipeline for automated 3D segmentation and morphometry of myelinated axons. The main developer has been Ali Abdollahzadeh. This pipeline is described in
Abdollahzadeh, A., Belevich, I., Jokitalo, E., Tohka, J. & Sierra, A. Automated 3D Axonal Morphometry of WhiteMatter. Scientific Reports 9, 6084 (2019)