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283
Unified segmentation
, 2005
"... A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and ..."
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Cited by 324 (12 self)
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A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
Voxel-based morphometry—The methods
- Neuroimage
, 2000
"... At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the ..."
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Cited by 273 (4 self)
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At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We 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. © 2000 Academic Press
Automated model-based tissue classification of MR images of the brain
, 1999
"... We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi ..."
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Cited by 214 (14 self)
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We describe a fully automated method for model-based tissue classification of Magnetic Resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities and incorporates contextual information by means of Markov Random Fields. A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.
Adaptive fuzzy segmentation of magnetic resonance images
- IEEE TRANS. MED. IMAG
, 1999
"... An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-me ..."
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Cited by 158 (10 self)
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An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
Automated model-based bias field correction in MR images of the brain
- IEEE Transactions on Medical Imaging
, 1999
"... Abstract — We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propos ..."
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Cited by 108 (10 self)
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Abstract — We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms. Index Terms—Bias field, digital brain atlas, MRI, tissue classification. I.
Improved watershed transform for medical image segmentation using prior information
- IEEE T-MI
, 2004
"... Abstract—The watershed transform has interesting properties that make it useful for many different image segmentation appli-cations: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has importan ..."
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Cited by 96 (4 self)
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Abstract—The watershed transform has interesting properties that make it useful for many different image segmentation appli-cations: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (overseg-mentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the wa-tershed transform that enables the introduction of prior informa-tion in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical vali-dation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation. Index Terms—Biomedical imaging, image segmentation, mor-phological operations, tissue classification, watersheds.
When keeping in mind supports later bringing to mind: neural markers of phonological rehearsal predict subsequent remembering
- J Cogn Neurosci
, 2001
"... Abstract & The ability to bring to mind a past experience depends on the cognitive and neural processes that are engaged during the experience and that support memory formation. A central and much debated question is whether the processes that underlie rote verbal rehearsal-that is, working mem ..."
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Cited by 79 (9 self)
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Abstract & The ability to bring to mind a past experience depends on the cognitive and neural processes that are engaged during the experience and that support memory formation. A central and much debated question is whether the processes that underlie rote verbal rehearsal-that is, working memory mechanisms that keep information in mind-impact memory formation and subsequent remembering. The present study used eventrelated functional magnetic resonance imaging (fMRI) to explore the relation between working memory maintenance operations and long-term memory. Specifically, we investigated whether the magnitude of activation in neural regions supporting the on-line maintenance of verbal codes is predictive of subsequent memory for words that were roterehearsed during learning. Furthermore, during rote rehearsal, the extent of neural activation in regions associated with semantic retrieval was assessed to determine the role that incidental semantic elaboration may play in subsequent memory for rote-rehearsed items. Results revealed that (a) the magnitude of activation in neural regions previously associated with phonological rehearsal (left prefrontal, bilateral parietal, supplementary motor, and cerebellar regions) was correlated with subsequent memory, and (b) while rote rehearsal did not-on average-elicit activation in an anterior left prefrontal region associated with semantic retrieval, activation in this region was greater for trials that were subsequently better remembered. Contrary to the prevalent view that rote rehearsal does not impact learning, these data suggest that phonological maintenance mechanisms, in addition to semantic elaboration, support the encoding of an experience such that it can be later remembered. &
Parametric estimate of intensity inhomogeneities applied to MRI
- IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2000
"... This paper presents a new approach to the correction of intensity inhomogeneities in Magnetic Resonance Imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentatio ..."
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Cited by 71 (1 self)
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This paper presents a new approach to the correction of intensity inhomogeneities in Magnetic Resonance Imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called PABIC (PArametric BIas field Correction) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a non-linear energy minimization problem using an Evolution Strategy. The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based ...
Reconstruction of the human cerebral cortex from magnetic resonance images
- IEEE Trans. Med. Imag
, 1999
"... Abstract—Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the req ..."
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Cited by 69 (11 self)
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Abstract—Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. In this paper we address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented. Index Terms—Cortical surface reconstruction, deformable surface models, fuzzy segmentation, isosurface, magnetic resonance imaging. I.
An Adaptive Fuzzy C-Means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities
- Pattern Recognition Letters
, 1998
"... We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy C-means algorithm to include a multiplier field, whic ..."
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Cited by 65 (6 self)
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We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy C-means algorithm to include a multiplier field, which allows the centroids for each class to vary across the image. First and second order regularization terms ensure that the multiplier field is both slowly varying and smooth. An iterative algorithm that minimizes the objective function is described, and its efficacy is demonstrated on several test images. Key words: image segmentation, fuzzy c-means, intensity inhomogeneities, magnetic resonance imaging 1 Introduction Image segmentation plays an important role in a variety of applications such as robot vision, object recognition, and medical imaging. There has been considerable interest recently in the use of fuzzy segmentation methods, which retain more information from the original im...
