A Review on MR Image Intensity Inhomogeneity Correction.
Reads0
Chats0
TLDR
This paper attempts to review some of the recent developments in the mathematical modeling of IIH field and summarizes other techniques based on different principles.Abstract:
Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.read more
Citations
More filters
Journal ArticleDOI
N4ITK: Improved N3 Bias Correction
Nicholas J. Tustison,Brian B. Avants,Philip A. Cook,Yuanjie Zheng,A Egan,Paul A. Yushkevich,James C. Gee +6 more
TL;DR: A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction with the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field Correction over the original N3 algorithm.
Journal ArticleDOI
Minimization of Region-Scalable Fitting Energy for Image Segmentation
TL;DR: This work proposes a region-based active contour model that draws upon intensity information in local regions at a controllable scale to cope with intensity inhomogeneity and shows desirable performances of this model.
Journal ArticleDOI
State of the art survey on MRI brain tumor segmentation.
TL;DR: Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation, and semiautomatic and fully automatic techniques are emphasized.
Journal ArticleDOI
An efficient local Chan-Vese model for image segmentation
TL;DR: Comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model show that the proposed LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.
Journal ArticleDOI
Lesion identification using unified segmentation-normalisation models and fuzzy clustering.
Mohamed L. Seghier,Anil F. Ramlackhansingh,Jennifer T. Crinion,Alexander P. Leff,Cathy J. Price +4 more
TL;DR: This work augments the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively and adopts a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments.
References
More filters
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Voxel-Based Morphometry—The Methods
John Ashburner,Karl J. Friston +1 more
TL;DR: In this paper, the authors describe the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with non-uniformity artifact and provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
Journal ArticleDOI
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.
Bruce Fischl,David H. Salat,Evelina Busa,Marilyn S. Albert,Megan E. Dieterich,Christian Haselgrove,Andre van der Kouwe,Ronald J. Killiany,David N. Kennedy,Shuna Klaveness,Albert Montillo,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.
Journal ArticleDOI
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.