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...tive clustering algorithm based on the current estimate of the longitudinal deformations in the image series, (2) it then refines these longitudinal deformations using a 4-D elastic warping algorithm =-=[15, 16]-=-. In this way, we obtain both a longitudinally-consistent segmentation result and an estimate of longitudinal deformation of anatomy in a series of 3-D images. The 4-D image-adaptive clustering algori...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...ince they process each image at a time. Herein, we propose a 4-D segmentation method that overcomes this limitation and significantly improves longitudinal stability of segmentation. Fuzzy algorithms =-=[1, 2, 3, 4, 5, 10]-=- have been proven to be more suitable for 3-D MR images than hard segmentation algorithms since the intensity of each voxel of an MR image may represent a combination of different tissues. Fuzzy C-Mea...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...ach voxel of an MR image may represent a combination of different tissues. Fuzzy C-Means (FCM) algorithms have been used in many segmentation applications often accounting for intensity inhomogeneity =-=[6, 7, 11, 12]-=- and incorporating spatial information among voxels [8, 13, 14]. The intensity inhomogeneity can be well modeled by the product of the original image and a gain field [12] or by the summation of G.E. ...

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...ach voxel of an MR image may represent a combination of different tissues. Fuzzy C-Means (FCM) algorithms have been used in many segmentation applications often accounting for intensity inhomogeneity =-=[6, 7, 11, 12]-=- and incorporating spatial information among voxels [8, 13, 14]. The intensity inhomogeneity can be well modeled by the product of the original image and a gain field [12] or by the summation of G.E. ...

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...The pre-processing of the input 3-D image series include: correct global intensity inhomogeneity [6] and globally normalize the intensities of each image according to the histogram of the first image =-=[17]-=-; transfer the subsequent images onto the space of the first image using rigid transformations. After preprocessing, CLASSIC is applied to consistently segment the rigidly aligned serial images It,t∈ ...

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...tissues. Fuzzy C-Means (FCM) algorithms have been used in many segmentation applications often accounting for intensity inhomogeneity [6, 7, 11, 12] and incorporating spatial information among voxels =-=[8, 13, 14]-=-. The intensity inhomogeneity can be well modeled by the product of the original image and a gain field [12] or by the summation of G.E. Christensen and M. Sonka (Eds.): IPMI 2005, LNCS 3565, pp. 101–...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, and evolution of pathology =-=[9]-=-. Consistent segmentation is particularly important in the literature of aging and Alzheimer’s Disease (AD) since subtle brain changes that might be indicative of early stages of underlying pathology ...

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...inhomogeneity (Pham and Prince, 1999; Chen and Giger, 2004; Guillemaud and Brady, 1998; Ahmed et al., 2002) and incorporating spatial information among voxels (Rezaee et al., 2000; Liew et al., 2000; =-=Pham, 2001-=-). The intensity of inhomogeneity can be well modeled by the product of the original image and a gain field (Ahmed et al., 2002) or by the summation of them (Pham and Prince, 1999). It is also desirab...

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...rent times or of a 4-D image is important in many neuroimaging studies that concentrate on normal development and aging, as well as on evolution of pathology (Resnick et al., 2000; Tang et al., 2001; =-=Freeborough and Fox, 1997-=-). In these studies, longitudinal stability is critical when measuring subject changes with time since true signal is overwhelmed by measurement error. However, existing 3-D segmentation algorithms ma...

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...tive clustering algorithm based on the current estimate of the longitudinal deformations in the image series, (2) it then refines these longitudinal deformations using a 4-D elastic warping algorithm =-=[15, 16]-=-. In this way, we obtain both a longitudinally-consistent segmentation result and an estimate of longitudinal deformation of anatomy in a series of 3-D images. The 4-D image-adaptive clustering algori...

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...ooth simulated images. The local atrophy is simulated by matching the Jacobian of the simulated deformation to the desired volumetric changes subject to smoothness and topology preserving constraints =-=[19]-=-. The amount of atrophy can be described by the shrinkage rate, 0 <rs <= 1. For example, rs =0.9 implies a 10% atrophy within the spherical area.108 Z. Xue, D. Shen, and C. Davatzikos t1 t5 t9 Fig. 2...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...t captured at different times or of a 4-D image is important in many neuroimaging studies that concentrate on normal development and aging, as well as on evolution of pathology (Resnick et al., 2000; =-=Tang et al., 2001-=-; Freeborough and Fox, 1997). In these studies, longitudinal stability is critical when measuring subject changes with time since true signal is overwhelmed by measurement error. However, existing 3-D...

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...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

Citation Context

...tissues. Fuzzy C-Means (FCM) algorithms have been used in many segmentation applications often accounting for intensity inhomogeneity [6, 7, 11, 12] and incorporating spatial information among voxels =-=[8, 13, 14]-=-. The intensity inhomogeneity can be well modeled by the product of the original image and a gain field [12] or by the summation of G.E. Christensen and M. Sonka (Eds.): IPMI 2005, LNCS 3565, pp. 101–...

Citation Context

...tion is a key processing step in many brain image analysis applications, e.g. morphometry, automatic tissue labeling, tissue volume quantification, image registration, and computer integrated surgery =-=[1, 2, 3, 4, 5, 6, 7, 8]-=-. Analysis of a series of 3-D data of the same subject captured at different time-points, i.e. of a 4-D image, is important in many neuroimaging studies that concentrate on normal development, aging, ...

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...for all the locations, and then adaptively adjust these sizes in every iteration: we segment the images using the current fuzzy membership functions, and then calculate the Fractional Anisotropy (FA) =-=[18]-=- of point (t, i) within the current neighborhood N (t,i), denoted as a (t,i). Since FA describes difference proportions of three tissue classes, the size of neighborhood N (t,i) is increased if its a ...