@article {1775,
	title = {A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps},
	journal = {Signal Image and Video Processing},
	volume = {11},
	year = {2017},
	pages = { 913-920},
	author = {P V Sudeep and Palanisamy, P and Chandrasekharan Kesavadas and Jan Sijbers and Arnold Jan den Dekker and Jeny Rajan}
}
@article {1481,
	title = {Iterative bilateral filter for Rician noise reduction in MR images},
	journal = {Signal, Image and Video Processing },
	year = {2014},
	doi = {10.1007/s11760-013-0611-6},
	author = {R. Riji and Jeny Rajan and Jan Sijbers and Madhu S. Nair}
}
@article {1441,
	title = {A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test},
	journal = {Signal Processing},
	volume = {103},
	year = {2014},
	pages = {16-23},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2013.12.018},
	author = {Jeny Rajan and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1369,
	title = {Comprehensive framework for accurate diffusion MRI parameter estimation},
	journal = {Magnetic Resonance in Medicine},
	volume = {81},
	year = {2013},
	pages = {972-984},
	doi = {10.1002/mrm.24529},
	author = {Jelle Veraart and Jeny Rajan and Ron R Peeters and Alexander Leemans and Stefan Sunaert and Jan Sijbers}
}
@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}
}
@inproceedings {1332,
	title = {An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images},
	booktitle = { IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2012},
	author = {Jeny Rajan and Johan Van Audekerke and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@mastersthesis {1378,
	title = {Estimation and removal of noise from single and multiple coil  Magnetic Resonance images},
	year = {2012},
	month = {11/2012},
	type = {PhD Thesis},
	author = {Jeny Rajan}
}
@article {1344,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images},
	journal = {Magnetic Resonance Imaging},
	volume = {30},
	year = {2012},
	pages = {1512-1518},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1438,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.},
	journal = {Magnetic resonance imaging},
	volume = {30},
	year = {2012},
	month = {2012 Dec},
	pages = {1512-8},
	abstract = {Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.},
	keywords = {Algorithms, Animals, Brain, Brain Mapping, Computer Simulation, Fourier Analysis, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Mice, Models, Statistical, Normal Distribution, Signal-To-Noise Ratio, Stochastic Processes},
	issn = {1873-5894},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1245,
	title = {Denoising magnitude MRI using an adaptive NLML method},
	year = {2011},
	pages = {383},
	address = {Leipzig, Germay},
	author = {Jeny Rajan and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {Kudzinava_2011_ESMRMB,
	title = {Denoising of DKI images: effect on feasibility and accuracy of kurtosis parameters},
	year = {2011},
	month = {October},
	pages = {236},
	address = {Leipzig, Germany},
	abstract = {Diffusion Kurtosis Imaging (DKI) is a new MRI technique, which quantifies non-Gaussianity of water diffusion. DKI requires high b-values, resulting in a low SNR of acquired diffusion weighted images (DWIs). Generally, kurtosis tensors are estimated from these DWIs using Weighted Least Squares (WLS) or Maximum Likelihood (ML) methods. In this work, we apply the Restricted Local Maximum Likelihood (RLML) denoising algorithm to remove noise from DWIs prior to WLS estimation. Then, we compare the RLML+WLS approach to the WLS and ML methods in terms of feasibility, that is the percentage of estimated tensors that satisfy certain DKI model constraints; and in terms of accuracy of calculated values of Mean Kurtosis.},
	keywords = {denoising, diffusion kurtosis imaging, maximum likelihood estimation},
	author = {Maryna Kudzinava and Jeny Rajan and Jan Sijbers}
}
@conference {1306,
	title = {Denoising SENSE reconstructed MR images},
	year = {2011},
	author = {Jeny Rajan and Jan Sijbers}
}
@conference {1331,
	title = {An extended NLML method for denoising non-central chi distributed data - application to parallel MRI},
	year = {2011},
	pages = {41},
	author = {Jeny Rajan and Johan Van Audekerke and Jelle Veraart and Marleen Verhoye and Jan Sijbers}
}
@article {jrajanbjeurissmverhoyeJ.jsijbers2011,
	title = {Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods},
	journal = {Physics in Medicine and Biology},
	volume = {56},
	number = {16},
	year = {2011},
	pages = {5221-5234},
	doi = {doi:10.1088/0031-9155/56/16/009},
	author = {Jeny Rajan and Ben Jeurissen and Marleen Verhoye and Johan Van Audekerke and Jan Sijbers}
}
@inproceedings {jrajanmverhoyejsijbers2011,
	title = {A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood},
	booktitle = {SPIE Medical Imaging},
	volume = {7962},
	year = {2011},
	publisher = {SPIE},
	organization = {SPIE},
	author = {Jeny Rajan and Marleen Verhoye and Jan Sijbers},
	editor = {B.M. Dawant and D.R. Haynor}
}
@conference {1256,
	title = {Robust edge directed interpolation of diffusion weighted MR images},
	year = {2011},
	pages = {382},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@conference {zmaijrajanmverhoyejsijbers2011,
	title = {Robust Edge-directed Interpolation: Application to Diffusion MR Images},
	year = {2011},
	month = {May},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {1261,
	title = {Robust edge-directed interpolation of magnetic resonance images},
	journal = {Physics in medicine and biology},
	volume = {56},
	year = {2011},
	pages = {7287-7303},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye 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 {jrajanbjeurissjsijbersK.2009,
	title = {Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	year = {2009},
	pages = {123-127},
	address = {Dublin, Ireland},
	author = {Jeny Rajan and Ben Jeurissen and Jan Sijbers and Keizer Kannan}
}
