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137
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 loglikelihood 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 loglikelihood 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.
A hybrid approach to the skull stripping problem in MRI
 NeuroImage
, 2004
"... We present a novel skullstripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single whit ..."
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Cited by 127 (11 self)
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We present a novel skullstripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the threedimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skullstripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally reestimated at the boundary of the brain. Finally, a highresolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skullstripping tools.
Improved watershed transform for medical image segmentation using prior information
 IEEE TMI
, 2004
"... Abstract—The watershed transform has interesting properties that make it useful for many different image segmentation applications: 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 applications: 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 (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information 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 validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation. Index Terms—Biomedical imaging, image segmentation, morphological operations, tissue classification, watersheds.
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 54 (11 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
Comparison and validation of tissue modelization and statistical classification methods
 in T1weighted MR brain images,” IEEE Trans. Med. Imag
, 2005
"... Abstract—This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assess ..."
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Cited by 44 (0 self)
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Abstract—This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods ’ results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data. Index Terms—Brain tissue models, hidden Markov random fields models, magnetic resonance imaging, partial volume, statistical classification, validation study. I.
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 43 (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.
Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and timedependent change. NeuroImage
 23(Supplement 1):S56–S68, 2004. Special Issue: Mathematics in Brain Imaging
"... Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of largedeformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical pr ..."
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Cited by 40 (12 self)
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Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of largedeformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical probability laws of anatomical variation between anatomies; and (iii) the construction of inferences regarding neuropsychiatric disease states. CA utilizes spatialtemporal vector field information obtained from largedeformation maps to assess anatomical variabilities and facilitate the detection and quantification of abnormalities of brain structure in subjects with neuropsychiatric disorders. Neuroanatomical structures are divided into two types: subcortical structuresgray matter (GM) volumes enclosed by a single surfaceand cortical mantle structuresanatomically distinct portions of the cerebral cortical mantle layered between the white matter (WM) and cerebrospinal fluid (CSF). Because of fundamental differences in the geometry of these two types of structures, imagebased largedeformation highdimensional brain mapping (HDBMLD) and largedeformation diffeomorphic metric matching (LDDMM) were developed for the study of subcortical structures and labeled cortical mantle distance mapping (LCMDM) was developed for the study of cortical mantle structures. Studies of neuropsychiatric disorders using CA usually require the testing of hypothesized group differences with relatively small numbers of subjects per group. Approaches that increase the power for testing such hypotheses include methods to quantify the shapes of individual structures, relationships between the shapes of related structures (e.g., asymmetry), and changes of shapes over time. Promising preliminary studies employing these approaches to studies of subjects with schizophrenia and very mild to mild Alzheimer's disease (AD) are presented. D 2004 Elsevier Inc. All rights reserved.
Multimodal classification of Alzheimer's disease and mild cognitive impairment
 Neuroimage
, 2011
"... This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. ..."
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Cited by 39 (12 self)
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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT
Isosurface Reconstruction with Topology Control
, 2002
"... Extracting isosurfaces from volumetric datasets is an essential step for indirect volume rendering algorithms. For physically measured data like it is used, e.g. in medical imaging applications one often introduces topological errors such as small handles that stem from measurement inaccuracy and ca ..."
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Cited by 37 (2 self)
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Extracting isosurfaces from volumetric datasets is an essential step for indirect volume rendering algorithms. For physically measured data like it is used, e.g. in medical imaging applications one often introduces topological errors such as small handles that stem from measurement inaccuracy and cavities that are generated by tight folds of an organ. During isosurface extraction these measurement errors result in a surface whose genus is much higher than that of the actual surface. In many cases however, the topological type of the object under consideration is known beforehand, e.g., the cortex of a human brain is always homeomorphic to a sphere. By using topology preserving morphological operators we can exploit this knowledge to gradually dilate an initial set of voxels with correct topology until it fits the target isosurface. This approach avoids the formation of handles and cavities and guarantees a topologically correct reconstruction of the object's surface.
A review on MR image intensity inhomogeneity correction
 International Journal of Biomedical Imaging
, 2006
"... 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. Lowfrequency models are widely used, but they tend to corr ..."
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Cited by 21 (0 self)
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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. Lowfrequency models are widely used, but they tend to corrupt the lowfrequency 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. Copyright © 2006 Zujun Hou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.