@inproceedings {1439,
	title = {A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images},
	booktitle = {5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings},
	volume = {8251},
	year = {2013},
	month = {2013},
	edition = {Lecture Notes in Computer Science},
	doi = {10.1007/978-3-642-45062-4_62},
	author = {Jeny Rajan and Arnold Jan den Dekker and Juntu, Jaber and Jan Sijbers}
}
@inbook {1263,
	title = {Classification of Soft Tissue Tumors by Machine Learning Algorithms},
	booktitle = {Soft Tissue Tumors},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	issn = {978-953-307-862-5},
	author = {Juntu, Jaber and Arthur M. De Schepper and Pieter Van Dyck and Dirk Van Dyck and Jan L Gielen and Paul M Parizel and Jan Sijbers}
}
@article {jjuntujsijberssdbackerjrajandvandyck2010,
	title = {A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {31},
	year = {2010},
	pages = {680{\textendash}689},
	doi = {10.1002/jmri.22095},
	author = {Juntu, Jaber and Jan Sijbers and Steve De Backer and Jeny Rajan and Dirk Van Dyck}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@inproceedings {DBLP:conf/iciar/RajanPJS10,
	title = {Segmentation Based Noise Variance Estimation from Background MRI Data},
	booktitle = {ICIAR },
	volume = {6111},
	year = {2010},
	month = {2010},
	pages = {62-70},
	publisher = {Springer LNCS},
	organization = {Springer LNCS},
	address = {Porto, Portugal},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@inproceedings {Juntu:2007:CST:1710523.1710591,
	title = {Classification of soft tissue tumors in MRI images using kernel PCA and regularized least square classifier},
	booktitle = {Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications},
	series = {SPPRA {\textquoteright}07},
	year = {2007},
	pages = {362{\textendash}367},
	publisher = {ACTA Press},
	organization = {ACTA Press},
	address = {Anaheim, CA, USA},
	keywords = {Fourier space, kernel PCA, MRI, RLSC, soft tissue tumors},
	isbn = {978-0-88986-647-8},
	url = {http://dl.acm.org/citation.cfm?id=1710523.1710591},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {jjuntujsijbersdvandyckGielen2005,
	title = {Bias Field Correction for MRI Images},
	booktitle = {Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05)},
	series = {Advances in Computer Science},
	year = {2005},
	month = {May},
	pages = {543-551},
	publisher = {Springer},
	organization = {Springer},
	address = {Rydzyna Castle, Poland},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck and Jan L Gielen},
	editor = {Marek Kurzynsji and Edward Puchala and Michal Wozniak and Andrzej Zolnierek}
}
