Jan Aelterman

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Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) 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)(More)
The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search(More)
Recently, there has been a huge interest in multiresolution representations that also perform a multidirectional analysis. The Shearlet transform provides both a multiresolution analysis (such as the wavelet transform), and at the same time an optimally sparse image-independent representation for images containing edges. Existing discrete implementations of(More)
Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction techniques that have recently been applied to increase MRI performance. In this paper we demonstrate that a combined analysis of the pMRI and CS problems leads to a conceptually simple, yet effective technique that outperforms independent approaches to both reconstruction(More)
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)(More)
We propose a new method for Magnetic Resonance Imaging (MRI) restoration. Because MR magnitude images are corrupted by Rician distributed noise, these images suffer from a contrast-reducing signal-dependent bias. Also the noise is often assumed to be white, however a widely used acquisition technique to decrease the acquisition time gives rise to correlated(More)
In this paper, we present a first-order primal-dual algorithm for tackling the joint demosaicking and deconvolution problem. The proposed algorithm exploits the sparsity of both discrete gradient (TV) and shearlet coefficients as prior knowledge. In order to deal with this sparsity across the color channels, we first decorrelate the signals in color space(More)
Cell nuclei detection in fluorescent microscopic images is an important and time consuming task in a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make individual nuclei detection a challenging task for automated image analysis. This paper proposes a novel and robust detection method based on the active(More)
Due to the ongoing miniaturization of digital camera sensors and the steady increase of the “number of megapixels”, individual sensor elements of the camera become more sensitive to noise, even deteriorating the final image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on(More)