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311
Multi-Atlas Segmentation without Registration: A Supervoxel-based Approach
"... Abstract. Multi-atlas segmentation is a powerful segmentation tech-nique. It has two components: label transfer that transfers segmentation labels from prelabeled atlases to a novel image and label fusion that com-bines the label transfer results. For reliable label transfer, most methods assume tha ..."
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Cited by 2 (0 self)
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Abstract. Multi-atlas segmentation is a powerful segmentation tech-nique. It has two components: label transfer that transfers segmentation labels from prelabeled atlases to a novel image and label fusion that com-bines the label transfer results. For reliable label transfer, most methods assume
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
- IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic
Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
"... Abstract. Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensity-based registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based ..."
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Abstract. Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensity-based registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based
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
A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity
"... Introduction: Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. ..."
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Cited by 1 (0 self)
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. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. Methods: High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label
Robust Patch-Based Multi-Atlas Labeling by Joint
"... Abstract. Automated labeling of anatomical structures on MR brain images is widely investigated in many neuroscience and clinic studies to quantitatively measure either individual or group structural/functional difference. To address the issue of registration errors that affect multi-atlas based lab ..."
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Abstract. Automated labeling of anatomical structures on MR brain images is widely investigated in many neuroscience and clinic studies to quantitatively measure either individual or group structural/functional difference. To address the issue of registration errors that affect multi-atlas based
Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation
"... Abstract. Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two independent ..."
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Cited by 1 (0 self)
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Abstract. Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two
Supervised method to build an atlas database for multiatlas segmentation-propagation, in: SPIE Medical Imaging, International Society for Optics and Photonics.
, 2010
"... ABSTRACT Multi-atlas based segmentation-propagation approaches have been shown to obtain accurate parcelation of brain structures. However, this approach requires a large number of manually delineated atlases, which are often not available. We propose a supervised method to build a population speci ..."
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Cited by 1 (0 self)
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specific atlas database, using the publicly available Internet Brain Segmentation Repository (IBSR). The set of atlases grows iteratively as new atlases are added, so that its segmentation capability may be enhanced in the multi-atlas based approach. Using a dataset of 210 MR images of elderly subjects
ORIGINAL ARTICLE Automated Segmentation of Mouse Brain Images Using Multi-Atlas Multi-ROI Deformation and Label Fusion
, 2012
"... Abstract We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of ..."
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Abstract We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region
Adaptive Segmentation of MRI data
, 1995
"... Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods requi ..."
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Cited by 224 (15 self)
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Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods
Results 1 - 10
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311
