R. Dann, J. Hoford, S. Kovacic, M. Reivich, and R. Bajcsy, "Evaluation of elastic matching systems for anatomic (CT, MR) and functional (PET) cerebral images," J. Comput. Assist. Tomogr., vol. 13, no. 4, pp. 603--611, Jul./Aug. 1989. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Volumetric Transformation of Brain Anatomy - Christensen, Joshi, Miller (1997) (43 citations) (Correct)
.... the mapping is based on geometric features, such as landmarks (points) 23] 9] and contours (lines) 24] 30] Alternatively, others have investigated volume mapping which use the image data directly to generate transformations throughout the coordinate system of the template and target [14] [15], 31] The voxel image data provide the matching forces throughout the continuum. In our work (see [32] both approaches are combined via a composition of transformations. The transformations are of high dimension, applied as if the template were a fluid, allowing for the examination of fine ....
R. Dann, J. Hoford, S. Kovacic, M. Reivich, and R. Bajcsy, "Evaluation of elastic matching systems for anatomic (CT, MR) and functional (PET) cerebral images," J. Comput. Assist. Tomogr., vol. 13, no. 4, pp. 603--611, Jul./Aug. 1989.
Deformable Models for Volume Feature Tracking - Klein (1999) (1 citation) (Correct)
....penalizing the square of the components of the flow field gradient was combined with an incompressibility constraint on the motion field to restrict the set of possible particle deformations. An analysis of the relative effectiveness of these constraints may be found in [36] Bajcsy and others [7,21,34,60] were among the first to incorporate a more realistic elastic material model as a regularization constraint. Though they used their deformation technique to match 3D volumes of brains from different people, the brains appeared similar enough so that a material model of continuous media gave ....
R Dann, J Hoford, S Kovacic, M Reivich, and R Bajcsy. "Evaluation of elastic matching system for anatomic (CT, MR) and functional PET cerebral images." J Comput Assist Tomogr, 13(4):603--611, 1989.
A Survey of Image Registration Techniques - Brown (1992) (27 citations) (Correct)
.... Structural features: graphs of subpattern configurations [Mohr 90] Syntactic features: grammars composed from patterns [Bunke 90] Semantic networks: scene regions and their relations [Faugeras 81] MATCHING AGAINST MODELS accurate intrinsic structure, noise in one image only Anatomic atlas [Dann 89] Geographic map [Maitre 87] Object model [Terzopoulos 87] Table 3: Feature Spaces used in Image Registration 40 ture spaces include: edges, contours, surfaces, other salient features such as corners, line intersections, and points of high curvature, statistical features such as moment ....
R. Dann, J. Hoford, S. Kovacic, M. Reivich and R. Bajcsy, "Evaluation of Elastic Matching System for Anatomic (CT, MR) and Functional (PET) Cerebral Images," J. of Computer Assisted Tomography, 13, July/August 1989, pp603-611.
3-D Deformable Registration Using a Statistical Atlas with.. - Chen (1999) (Correct)
....validation: ground truth segmentation of anatomical structures in living people is not available. As a solution, expert segmentation and classification of a subject s anatomical structures are regarded as ground truth, or the gold standard. A similar approach was employed in [2] 14] [19], and [33] The ground truth used in this thesis is comprised of 40 subjects MRI volumes that have expert segmentation of one structure, corpus callosum, in one plane, the mid sagittal plane. Figure 23 shows an example. The dimensionality of the test set is listed in Table 1. The experts are ....
....Measurement Currently, there is no standard metric for evaluating segmentation accuracy. Dann et al. introduced a relative overlap measure for comparing two segmentations when neither is necessarily correct. It is defined as the ratio between the area of intersection and the area of their union [19]. Collins et al. used three measures to evaluate segmentation accuracy [14] One is the ratio of absolute volume difference between ground truth and the computed segmentation w.r.t. ground truth. Since a small volume difference does not indicate accurate segmentation, another measure is defined as ....
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Dann et al., "Evaluation of elastic matching systems for anatomic (CT, MR) and functional (PET) cerebral images", Journal of Computer Assisted Tomography, vol. 13, No. 4, pp 603-611, 1989.
Bayesian Approach to the Brain Image Matching Problem - Gee, LeBriquer, Barillot, .. (1995) (8 citations) Self-citation (Bajcsy) (Correct)
....appealing because the computational complexity is substantially less than that involved in imposing higher order continuity on our mappings. At the same time, the accuracy of the results obtained with these mappings do not appear to be compromised, at least, in the evaluation studies reported in [16, 17, 18]. There is currently no empirical evidence to suggest that higher order smoothness constraints are absolutely necessary in brain image matching. A more important consideration in many applications of matching appears to be the prior modeling of 2 The intensity difference in Eqn. 1 is not squared ....
R. Dann, J. Hoford, S. Kovacic, M. Reivich, and R. Bajcsy, "Evaluation of elastic matching system for anatomic (CT, MR) and functional (PET) cerebral images," J Comput Assist Tomogr, vol. 13, pp. 603--611, 1989.
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