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25
Voxelbased morphometry using the ravens maps: Methods and validation using simulated longitudinal atrophy
 NeuroImage
, 2001
"... Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in populationbased studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extensi ..."
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Cited by 63 (18 self)
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Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in populationbased studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extension and validation of a previously published methodology, referred to as RAVENS, for characterizing regional atrophy in the brain. A new method for elastic, volumepreserving spatial normalization, which allows for accurate quantification of very localized atrophy, is used. The RAVENS methodology was tested on images with simulated atrophy within two gyri: precentral and superior temporal. It was found to accurately determine the regions of atrophy, despite their localized nature and the interindividual variability of cortical structures. Moreover, it was found to perform substantially better than the voxelbased morphology method of SPM’99. Improved sensitivity was achieved at the expense of human effort involved in defining a number of sulcal curves that serve as constraints on the 3D elastic warping. © 2001 Academic Press
Automated graphbased analysis and correction of cortical volume topology
 IEEE Trans Med Imaging
, 2001
"... Abstract—The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Lowlevel segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical ..."
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Cited by 40 (0 self)
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Abstract—The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Lowlevel segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a lowlevel tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects. Index Terms—Magnetic resonance imaging, topological correction, topology, segmentation. I.
Fast and robust parameter estimation for statistical partial volume models in brain MRI
 NEUROIMAGE
, 2004
"... Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received ..."
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Cited by 20 (10 self)
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Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation–maximizationlike (EMlike) procedure for simultaneous parameter and
Combining Elastic and Statistical Models of Appearance Variation
, 2000
"... We propose a model of appearance and a matching method which combines `global' models (in which a few parameters control global appearance) with local elastic or opticalflowbased methods, in which deformation is described by many local parameters together with some regularisation constraints. ..."
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Cited by 11 (0 self)
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We propose a model of appearance and a matching method which combines `global' models (in which a few parameters control global appearance) with local elastic or opticalflowbased methods, in which deformation is described by many local parameters together with some regularisation constraints. We use an Active Appearance Model (AAM) as the global model, which can match a statistical model of appearance to a new image rapidly. However, the amount of variation allowed is constrained by the modes of the model, which may be too restrictive (for instance when insufficient training examples are available, or the number of modes is deliberately truncated for efficiency or memory conservation). To compensate for this, after global AAM convergence, we allow further local model deformation, driven by local AAMs around each model node. This is analogous to optical ow or `demon' methods of nonlinear image registration. We describe the technique in detail, and demonstrate that allowing...
Logarithm odds maps for shape representation
 IN PROC. MICCAI, VOLUME II
, 2006
"... The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates desirable properties for medical imaging. For example, the representation encodes the shape ..."
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Cited by 11 (0 self)
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The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates desirable properties for medical imaging. For example, the representation encodes the shape of an anatomical structure as well as the variations within that structure. These variations are embedded in a vector space that relates to a probabilistic model. We apply our representation to a voxel based segmentation algorithm. We do so by embedding the manifold of Signed Distance Maps (SDM) into the linear space of LogOdds. The LogOdds variant is superior to the SDM model in an experiment segmenting 20 subjects into subcortical structures. We also use LogOdds in the nonconvex interpolation between space conditioned distributions. We apply this model to a longitudinal schizophrenia study using quadratic splines. The resulting timecontinuous simulation of the schizophrenic aging process has a higher accuracy then a model based on convex interpolation.
Towards a Hybrid System Using an Ontology Enriched by Rules for the Semantic Annotation
 of Brain MRI Images, Web Reasoning and Rule Systems, LNCS 4524
"... Abstract. This paper describes an hybrid method combining symbolic and numerical techniques for annotating brain Magnetic Resonance images. Existing automatic labelling methods are mostly statistical in nature and do not work very well in certain situations such as the presence of lesions. The goal ..."
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Cited by 7 (3 self)
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Abstract. This paper describes an hybrid method combining symbolic and numerical techniques for annotating brain Magnetic Resonance images. Existing automatic labelling methods are mostly statistical in nature and do not work very well in certain situations such as the presence of lesions. The goal is to assist them by a knowledgebased method. The system uses statistical method for generating a sufficient set of initial facts for fruitful reasoning. Then, the reasoning is supported by an OWL DL ontology enriched by SWRL rules. The experiments described were achieved using the KAON2 reasoner for inferring the annotations. 1
Mri tissue classification with neighborhood statistics: A nonparametric, entropyminimizing approach
 in Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention (MICCAI
, 2005
"... Abstract. We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropybased metric de ..."
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Cited by 6 (1 self)
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Abstract. We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropybased metric defined on high dimensional probability density functions. Combined with an atlasbased initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches. 1
Shape based segmentation of anatomical structures in magnetic resonance images
 IN ICCV, VOL. 3765 OF LNCS
, 2005
"... Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partia ..."
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Cited by 3 (1 self)
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Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an ExpectationMaximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.
Selfprojection and the brain
, 2007
"... Automatic segmentation of MR images of the developing ..."
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Cited by 3 (0 self)
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Automatic segmentation of MR images of the developing