@article {1934,
	title = {Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	chapter = {624},
	abstract = {Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available},
	keywords = {Classification, decision fusion, Hyperspectral},
	doi = {https://doi.org/10.3390/rs11060624},
	url = {https://www.mdpi.com/2072-4292/11/6/624},
	author = {Vera Andrejchenko and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@article {1909,
	title = {Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks},
	journal = {Medical Image Analysis},
	volume = {52},
	year = {2019},
	pages = {56-67},
	doi = {https://doi.org/10.1016/j.media.2018.10.009},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Jan Sijbers and Alexander Leemans}
}
@proceedings {1899,
	title = {ACIVS 2018, Advanced Concepts for Intelligent Vision Systems },
	volume = {11182},
	year = {2018},
	author = {J Blanc-Talon and D Helbert and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {1870,
	title = {MRF-based decision fusion for hyperspectral image classification},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	address = {Valencia, Spain},
	abstract = {The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome
of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along
with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are
fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral
images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that
the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.},
	url = {https://www.igarss2018.org/Papers/viewpapers.asp?papernum=2751},
	author = {Vera Andrejchenko and Rob Heylen and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@proceedings {1904,
	title = {ACIVS 2017, Advanced Concepts for Intelligent Vision Systems},
	volume = {10617},
	year = {2017},
	author = {J Blanc-Talon and R Penne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1803,
	title = {Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {10},
	year = {2017},
	pages = {3768-3781},
	author = {Renbo Luo and Wenzhi Liao and H Zhang and L Zhang and Y Pi and Paul Scheunders and Wilfried Philips}
}
@proceedings {1905,
	title = {ACIVS 2016, Advanced Concepts for Intelligent Vision Systems},
	volume = {10016},
	year = {2016},
	author = {C Distante and J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {1717,
	title = {Classification of hyperspectral images with very small training size using sparse unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2016},
	address = {Beijing, China},
	author = {Vera Andrejchenko and Rob Heylen and Paul Scheunders and Wilfried Philips and Wenzhi Liao}
}
@article {1651,
	title = {D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data},
	journal = {PlosOne},
	volume = {11},
	number = {3},
	year = {2016},
	pages = {1-23},
	doi = {10.1371/journal.pone.0149778},
	author = {Daniele Perrone and Ben Jeurissen and Jan Aelterman and Timo Roine and Jan Sijbers and Aleksandra Pizurica and Alexander Leemans and Wilfried Philips}
}
@inproceedings {1810,
	title = {Lidar information extraction by attribute filters with partial reconstruction},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 1484-1487, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7729379},
	author = {Wenzhi Liao and M Della Mura and X Huang and Jocelyn Chanussot and S Gautama and Paul Scheunders and Wilfried Philips}
}
@proceedings {1906,
	title = {ACIVS 2015, Advanced Concepts for Intelligent Vision Systems},
	volume = {9386},
	year = {2015},
	author = {S Battiato and J Blanc-Talon and G Gallo and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {perrone2015effect,
	title = {The effect of Gibbs ringing artifacts on measures derived from diffusion MRI},
	journal = {NeuroImage},
	volume = {120},
	year = {2015},
	pages = {441{\textendash}455},
	publisher = {Academic Press},
	author = {Daniele Perrone and Jan Aelterman and Pi{\.z}urica, Aleksandra and Ben Jeurissen and Wilfried Philips and Alexander Leemans}
}
@article {1519,
	title = {Informed constrained spherical deconvolution (iCSD)},
	journal = {Medical Image Analysis},
	volume = {24},
	year = {2015},
	pages = {269{\textendash}281},
	type = {Original research},
	chapter = {269},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.},
	keywords = {Connectomics, constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect, Response function, Tractography},
	doi = {10.1016/j.media.2015.01.001},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841515000080},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {roine2015isotropic,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Information-based methods for neuroimaging: analyzing structure, function and dynamics},
	year = {2015},
	pages = {112},
	publisher = {Frontiers Media SA},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@inproceedings {1635,
	title = {Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {W. Liao and F. Huang and F. v. Coillie and Guy Thoonen and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@conference {roine2014ismrm,
	title = {Improving fiber orientation estimation in constrained spherical deconvolution under non-white matter partial volume effects},
	year = {2014},
	month = {05/2014},
	pages = {4492},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@conference {1443,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	year = {2014},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {roine2014frontiers,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Frontiers in Neuroinformatics},
	volume = {8},
	number = {28},
	year = {2014},
	month = {03/2014},
	pages = {1-9},
	publisher = {Frontiers},
	type = {Original Research},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVE) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple nonparallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. 

In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNR), fiber configurations, and tissue fractions. 

Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50 \% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50 \% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25 \% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. 

The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
},
	keywords = {constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect},
	doi = {10.3389/fninf.2014.00028},
	url = {http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00028/abstract},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@conference {1559,
	title = {A Novel Method for Realistic DWI Data Generation},
	volume = {22},
	year = {2014},
	pages = {4427},
	address = {Milan, Italy},
	author = {Daniele Perrone and Jan Aelterman and Ben Jeurissen and Aleksandra Pizurica and Wilfried Philips and Jan Sijbers}
}
@proceedings {1907,
	title = {ACIVS 2013, Advanced Concepts for Intelligent Vision Systems},
	volume = {8129},
	year = {2013},
	author = {J Blanc-Talon and A Kazinski and Wilfried Philips and D Popescu and Paul Scheunders}
}
@conference {1433,
	title = {Analysis and modeling of isotropic partial volume effects in diffusion MRI},
	volume = {7},
	year = {2013},
	month = {2013},
	doi = {10.3389/conf.fninf.2013.10.00027},
	author = {Timo Roine and Ben Jeurissen and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@article {1347,
	title = {Semi-supervised local discriminant analysis for feature extraction in hyperspectral images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {184-198},
	author = {W. Liao and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips and Y Pi}
}
@proceedings {1373,
	title = {ACIVS 2012, Advanced Concepts for Intelligent Vision Systems},
	volume = {7517},
	year = {2012},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders and P. Zemcik}
}
@conference {1325,
	title = {Correction of Gibbs ringing in diffusion MRI data using total variation regularization},
	year = {2012},
	month = {January},
	pages = {99},
	keywords = {MRI distortion artefacts},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@proceedings {1235,
	title = {ACIVS 2011, Advanced Concepts for Intelligent Vision Systems},
	volume = {6915},
	year = {2011},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and R. Kleihorst and Paul Scheunders}
}
@inproceedings {WitteThoonenpscheundPizuricaPhilips2011,
	title = {Classification of multi-source images using color mathematical morphological profiles},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International},
	year = {2011},
	pages = {3919 - 3922},
	address = {Vancouver, BC, Canada},
	abstract = {In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.},
	keywords = {Classification, Color, Morphological profiles, Multisource images, remote sensing},
	isbn = {978-1-4577-1003-2},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2011.6050088},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6050088\&isnumber=6048881},
	author = {Val{\'e}rie De Witte and Guy Thoonen and Paul Scheunders and Aleksandra Pizurica and Wilfried Philips}
}
@conference {1307,
	title = {Distortion correction of DKI data: affine approach},
	year = {2011},
	month = {December},
	abstract = {In this work, the performance of FMRIB{\textquoteright}s Linear Registration Tool (FLIRT) is evaluated with respect to motion and distortion correction of diffusion kurtosis imaging (DKI) data. FLIRT is tested on two DKI datasets heavily distorted by motion and eddy currents artefacts. Six different criteria are used for evaluation. Our results show that distortion correction using FLIRT substantially improves the original data. However, being an affine registration tool, FLIRT cannot cope with the amount of non-linear distortion present in DKI datasets. Hence, we conclude that, for correction of DKI data, non-linear registration techniques are more suitable.},
	keywords = {medical imaging},
	author = {Maryna Kudzinava and Alexander Leemans and Wilfried Philips and Daniele Perrone and Jan Aelterman and J. Dudink and Jan Sijbers}
}
@conference {1308,
	title = {Gibbs artifact suppression for DT-MRI data},
	year = {2011},
	month = {December},
	keywords = {medical imaging},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@proceedings {1236,
	title = {ACIVS 2010, Advanced Concepts for Intelligent Vision Systems},
	volume = {6474 \& 6475},
	year = {2010},
	publisher = {Springer},
	author = {J Blanc-Talon and D. Bone and Wilfried Philips and D Popescu and Paul Scheunders}
}
@proceedings {1237,
	title = {ACIVS 2009, Advanced Concepts for Intelligent Vision Systems},
	volume = {5807},
	year = {2009},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@proceedings {1238,
	title = {ACIVS 2008, Advanced Concepts for Intelligent Vision Systems},
	volume = {5259},
	year = {2008},
	author = {J Blanc-Talon and S. Bourenanne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {sdbackerPizuricaHuysmansPhilipspscheund2008,
	title = {Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators},
	journal = {Image and Vision Computing},
	volume = {26},
	number = {7},
	year = {2008},
	month = {July},
	pages = {1038-1051},
	author = {Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips and Paul Scheunders}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2007,
	title = {ACIVS 2007, Advanced Concepts for Intelligent Vision Systems},
	volume = {4678},
	year = {2007},
	pages = {1-1095},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {pscheundsdbackerPizuricaHuysmansPhilips2007,
	title = {Bayesian Wavelet-based Denoising of Multicomponent Images},
	booktitle = {Proceedings Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, 9-12 september, Vol. 6763-OK (12 pages)},
	year = {2007},
	author = {Paul Scheunders and Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2006,
	title = {ACIVS 2006, Advanced Concepts for Intelligent Vision Systems},
	volume = {4179},
	year = {2006},
	pages = {1-1224},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2005,
	title = {ACIVS 2005, Advanced Concepts for Intelligent Vision Systems},
	volume = {3708},
	year = {2005},
	pages = {1-725},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {PizuricaHuysmanspscheundPhilips2005,
	title = {Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context},
	booktitle = {IEEE International Geoscience and Remote Sensing Symp. IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {6},
	year = {2005},
	pages = {4260-4263},
	address = {Seoul, Korea},
	author = {Aleksandra Pizurica and B. Huysmans and Paul Scheunders and Wilfried Philips}
}
@inproceedings {PizuricaPhilipspscheund2005,
	title = {Wavelet domain denoising of single-band and multi-band images adapted to the probability of the presence of features of interes},
	booktitle = {SPIE Wavelets XI, San Diego, California, USA, 31 July {\textendash} 4 Aug},
	volume = {5914},
	year = {2005},
	pages = {508-521},
	author = {Aleksandra Pizurica and Wilfried Philips and Paul Scheunders}
}
@inproceedings {PizuricapscheundPhilips2004,
	title = {Multiresolution multispectral image denoising based on probability of presence of features of interest},
	booktitle = {Advanced Concepts in Image and Vision Systems, Brussels},
	year = {2004},
	pages = {357-364},
	author = {Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
