A Unified Framework for Atlas Matching Using Active Appearance Models

Cootes, Timothy F.; Beeston, C.; Edwards, Gillian; Taylor, Christopher J.

doi:10.1007/3-540-48714-x_24

Cited by 112 publications

(107 citation statements)

References 18 publications

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“…Furthermore, by utilizing a hierarchical (multi-scale) and regional principal component analysis to capture the shape variation statistics in a training set (Hamarneh and McInerney, 2001), we can keep the deformations consistent with prior knowledge of possible shape variations. Whereas general statistically-derived shape models produce only global shape variation modes (Cootes et al, 1999;Szekely et al, 1996), we are able to produce spatially-localized feasible deformations at desired scales, thus supporting our goal of intelligent deformation planning.…”

Section: Motor Systemmentioning

confidence: 63%

“…The models are fitted to images by minimizing energy functions, simulating dynamical systems, or applying probabilistic inference methods, but they do not control this optimization process other than in primitive ways, such as monitoring convergence or equilibrium. Some deformable models incorporate prior information to constrain shape and image appearance and the observed statistical variation of these quantities (Cootes et al, 1995(Cootes et al, , 1999Szekely et al, 1996). These models have no explicit awareness of where they or their parts are, and therefore the effectiveness of such constraints is dependent upon appropriate model initialization.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Furthermore, because there typically is no active, deliberate search for stable image features, the models can latch onto nearby spurious features (Cootes et al, 1999). Their short-sighted decision-making abilities prevent these models from correcting missteps.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

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Deformable organisms for automatic medical image analysis

McInerney

Hamarneh

Shenton

et al. 2002

Medical Image Analysis

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We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose 'deformable organisms', autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a 'corpus callosum worm' that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.

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Section: Motor Systemmentioning

confidence: 63%

Section: Motivation and Backgroundmentioning

confidence: 99%

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Deformable organisms for automatic medical image analysis

McInerney

Hamarneh

Shenton

et al. 2002

Medical Image Analysis

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“…Our method is similar to Yao's in representing bones by tetrahedral meshes, with bone shape determined by the vertex coordinates and radiological density within each tetrahedron represented by barycentric Bernstein polynomials. Following Yao, we create a statistical atlas by deforming a standard template mesh to match multiple patient images and performing standard analysis to extract principal components of variation of shape (vertex coordinates) and density (polynomial coefficients), in a manner similar to [7].…”

Section: Related Workmentioning

confidence: 99%

Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Sadowsky

Ramamurthi

Ellingsen

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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We present a method to improve the quality of cone-beam tomographic images computed from an intra-operative C-arm scan by adding information from an anatomical atlas. Limited range of C-arm view angles leads to reconstruction artifacts and poor anatomical detail. We propose to complete the missing views with simulated projections of a statistical anatomical model, which is deformably registered to match the data in the C-arm images. This paper presents the methods used to create the atlas and to register it with x-ray images. We compare the results of seven leave-one-out simulated hybrid reconstruction tests on a population of 13 subjects, with "ground-truth" CT, a classical short-scan, and a partial-scan reconstruction.

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“…Szeliski [9] introduced the statistical atlas to analyze the shape variation between patients. In order to analyze the shape and appearance variation, principal component analysis (PCA) is widely used [10,11,12]. The two most common statistical atlases are the shape atlas [14] and the appearance atlas [13].…”

Section: Introductionmentioning

confidence: 99%

Femur statistical atlas construction based on two-level 3D non-rigid registration

Wu¹,

Murtha²,

Jaramaz³

2009

Comp. Aided Surgery

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The statistical atlas is a 3D medical image analysis tool towards more patient-oriented and more efficient diagnosis. The atlas includes information on geometry and their variation across populations. The comparison and information from other patients is very useful for the objective quantitative diagnosis. The statistical atlas can also be used to solve other challenging problems such as image segmentation. As a key to build statistical atlases, 3D registration remains an important yet unsolved problem in the medical image field due to the geometrical complexity of anatomical shapes and computational complexity caused by the enormous size of volume data.Method: In this work we developed a two-level framework to efficiently solve 3D nonrigid registration and applied the method to the problem of building statistical atlases of femur. Compared with a general multi-resolution framework, we employed an interpolation to propagate the matching instead of repeating the registration scheme in each resolution. The registration procedure is divided into two levels: a low-resolution solution to the correspondences and mapping of surface models using Chui and Rangarajan's thin-plate spline (TPS) based algorithm [25], followed by an interpolation to achieve high-resolution matching. After that, principal component analysis (PCA) is used to build the statistical atlas. Experimental results show the shape variation learned from the atlas, and also demonstrate that out method significantly improves efficiency of registration without decreasing accuracy of atlases.

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A Unified Framework for Atlas Matching Using Active Appearance Models

Cited by 112 publications

References 18 publications

Deformable organisms for automatic medical image analysis

Deformable organisms for automatic medical image analysis

Atlas-Assisted Tomography: Registration of a Deformable Atlas to Compensate for Limited-Angle Cone-Beam Trajectory

Femur statistical atlas construction based on two-level 3D non-rigid registration

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