A Review of Methods for Correction of Intensity Inhomogeneity in MRI

By U. Vovk, Franjo Pernus, Bostjan Likar. IEEE Transactions on Medical Imaging, 2007

923 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5eba6400de4ed990704efac9c4fad055350dbf4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “This bias field induces variations in the intensity of the same tissue in different locations of the image, which deteriorates the performance of image analysis algorithms such as registration (Vovk et al., 2007).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, background): “However, some image artefacts like the partial volume effect, image noise and intensity non-uniformities (also known as bias field [110]) can considerably increase the difficulty of segmentation work.”
Computer Vision, Graphics, and Image Processing (2016, methodology): “The work proposed in [4] expresses the object detection in video as an optimal salient path discovery problem.”
An efficient local Chan-Vese model for image segmentation (2010, background): “The generally accepted assumption on intensity inhomogeneity is that it manifests itself as a smooth spatial varying function over the image [37].”
N4ITK: Improved N3 Bias Correction (2010, neutral): “For a review of intensity inhomogeneity artifacts and a relatively recent discussion of existing correction methods, see [1]–[3] and the references contained therein.”
State of the art survey on MRI brain tumor segmentation. (2013, methodology): “Reviews of these methods can be found in [31 – 33].”
Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging (2013, methodology): “IIH correction methods (Vovk et al., 2007) reduce the smooth variation of intensity of the tissues to simplify subsequent segmentation, although some segmentation methods take this effect into account (Ashburner and Friston, 2005; Van Leemput et al.”
Review of brain MRI image segmentation methods (2010, background): “Phantom Based: Inhomogeneity field can be estimated by taking the image of a uniform phantom, then, scaling and smoothing the image (Vovk et al. 2007).”
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, methodology): “As a consequence, the SI of identical tissues varies depending on their spatial location in the image making further processes such as segmentation or registration harder [108,109].”