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A Unified Framework for Joint Registration and Segmentation
, 2008
"... Accurate image registration is a necessary prerequisite for many diagnostic and therapy planning procedures where complementary information from different images has to be combined. The design of robust and reliable non-parametric registration schemes is currently a very active research area. Modern ..."
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present a novel unified framework for non-parametric registration combined with energybased segmentation through active contours. In the literature, one may find various ways to combine these image processing routines. Here, we present the most promising approaches within the general framework
Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images
, 2013
"... Abstract. In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical m ..."
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Cited by 5 (3 self)
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Abstract. In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical
Effects of registration regularization and atlas sharpness on segmentation accuracy,” Med
- Image Anal
, 2008
"... Abstract. In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of “sharpness ” and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image fidelit ..."
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Cited by 24 (10 self)
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Abstract. In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of “sharpness ” and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image
Building Spatiotemporal Anatomical Models using Joint 4-D Segmentation, Registration, and Subject-Specific Atlas Estimation
"... Longitudinal analysis of anatomical changes is a vital component in many personalized-medicine applications for predicting disease onset, determining growth/atrophy patterns, evaluating disease progression, and monitoring recovery. Estimating anatomical changes in longitudinal studies, especially th ..."
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Cited by 3 (2 self)
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for constructing subject-specific longitudinal anatomical models. The proposed method solves a generalized problem of joint segmentation, registration, and subjectspecific atlas building, which involves not just two images, but an entire longitudinal image sequence. The proposed framework describes a novel
Automatic Brain Segmentation in Rhesus Monkeys
"... Many neuroimaging studies are applied to primates as pathologies and environmental exposures can be studied in well-controlled settings and environment. In this work, we present a framework for both the semi-automatic creation of a rhesus monkey atlas and a fully automatic segmentation of brain tiss ..."
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and parcellation procedure was successfully re-applied to all training images, as well as applied to over 100 additional images. The deformable registration was able to identify corresponding cortical sulcal borders accurately. Even though the individual methods used in this segmentation framework have been
Accepted to MICCAI 2007 Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy
"... Abstract. In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of “sharpness ” and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image fidelit ..."
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Abstract. In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of “sharpness ” and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image
Author manuscript, published in "ICCV- 14th International Conference on Computer Vision (2013)" Uncertainty-driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration
, 2013
"... Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the di ..."
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graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate
Supervised Nonparametric Image Parcellation
"... Abstract. Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-su ..."
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Cited by 2 (1 self)
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image alignment tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white matter
ARTICLE NO. NI970290 Multimodal Image Coregistration and Partitioning—A Unified Framework
, 1997
"... This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate withinmodality registrations to two template ima ..."
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This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate withinmodality registrations to two template
