Parametric estimate of intensity inhomogeneities applied to MRI

By Martin Andreas Styner, Christian M. Brechbühler, G. Székely, Guido Gerig. IEEE Transactions on Medical Imaging, 2000

607 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/850e23b6aeabda059b6940956566e2c36f33ee98

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Frontal-hippocampal double dissociation between normal aging and Alzheimer's disease. (2005, methodology): “Intensity inhomogeneity was corrected using an algorithm minimizing intensity variation within continuous regions with the bias field modeled as a general secondorder polynomial in x, y, z (10 free parameters) ( Styner, Brechbuhler, Szekel and Gerig, 2000 ).”
Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation (2004, neutral): “Although such physical and digital phantoms [2], [3] have an important role to play in quantifying algorithm performance, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms that reproduce the full range of imaging characteristics (such as partial…”
Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults (2007, methodology): “Intensity variation was corrected across contiguous regions, based on a quadratic inhomogeneity model fitted to data from a phantom ( Styner, Brechbuhler, Szekely, & Gerig, 2000 ).”
A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume (2004, positive): “Normalized whole-brain volume measurement Normalized whole-brain volume (nWBV) was computed by first intensity normalizing the MP-RAGE data based on Styner et al. (2000) and then using a validated segmentation tool to classify brain tissue as cerebral spinal fluid (CSF), gray, or white matter…”
MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field (2010, methodology): “This can be done either by low-pass filtering (Cohen et al., 2000; Wald et al., 1995) or by fitting slowly varying functions such as Gaussians or low order polynomials (Styner et al., 2000).”
Learning Visual Affordances for Robotic Manipulation (2019, methodology): “The 2D pipeline starts by fixing minor possible image misalignments by using multimodal 2D intensity-based registration to align the two RGB-D images [111].”
An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation (2003, methodology): “[14] and is consistent with the spatial inhomogeneity arising from the variation in the sensitivity of the RF coils and the nonuniform excitations.”
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, background): “Bias correction: Besides being corrupted by noise, MRI images are also affected by the inhomogeneity of the MRI field commonly referred to as bias field [107].”
A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (2016, neutral): “Therefore, our method achieves a soft classification, satisfying the condition of the partial volume effect [25] (i.”
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge (2016, methodology): “The 2D pipeline starts by fixing minor possible image misalignments by using multimodal 2D intensity-based registration to align the two RGB-D images [21].”
BioImage Suite: An integrated medical image analysis suite: An update. (2006, methodology): “…implemented and customized the following algorithms: Pre-processing: A custom reimplementation of the bias field correction method of Styner et al. [22] has been performed which incorporates automated histogram fitting for determining the appropriate numbers of classes, and additional spatial…”
Automatic segmentation of MR images of the developing newborn brain (2005, methodology): “(Styner et al., 2000), are likely to have difficulties in obtaining the optimal solution.”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, methodology): “Intensity normalization methods such as bias field correction [27, 71, 74] can work poorly in realistic situations (e.”