Non-rigid Surface Registration Using Spherical Thin-Plate Splines

Zou, Guangyu; Hua, Jing; Muzik, Otto

doi:10.1007/978-3-540-75757-3_45

Cited by 31 publications

(19 citation statements)

References 13 publications

(16 reference statements)

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“…We use an extrinsic registration method based on Thin-Plate Splines (TPS) [30,43] which uses the intensity information along with some landmarks selected on apex, epicardium and endocardium surfaces, and register the LV to the template.…”

Section: Experimental Results On Real Imaging Datamentioning

confidence: 99%

Visualization of Shape Motions in Shape Space

Taimouri

Hua

2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying threedimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods.

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Section: Experimental Results On Real Imaging Datamentioning

confidence: 99%

Visualization of Shape Motions in Shape Space

Taimouri

Hua

2013

IEEE Trans. Visual. Comput. Graphics

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“…Subsequently, the intersubject and intra-subject registrations are preformed to find the corresponding points among different meshes. To accomplish this, a total of 16 landmarks are chosen on meshes manually (Fig.3), and then the constrained thin plate spline method is used to perform the registration [8,9]. Although the noise distribution of the gated images is approximately Poisson, it might be estimated as Gaussian as well, depending on the number of channels used during the image acquisition.…”

Section: Application On Heart Motion Analysismentioning

confidence: 99%

Comparative Analysis of Quasi-Conformal Deformations in Shape Space

Taimouri

Hua

2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract.A novel approach based on the shape space concept is proposed to classify quasi-conformal deformations of 3D models. A new metric on the quotient space of meshes is introduced to capture changes of the curvature at each vertex of a simplicial complex during deformation. Then, the deformation curve is obtained by calculating the geodesic curve connecting two shapes in the shape space manifold. In order to compare the deformations, the deformation curves are first transferred to the same part of shape space. And then, the Multi-Dimensional Scaling method is employed to eliminate the redundant dimensions facilitating easy comparison of the deformations. To evaluate our method, some synthetic datasets and 23 datasets of gated images of the left heart ventricle during one heartbeat have been examined. Our experiments show that the algorithm can effectively classify normal and abnormal left heart ventricle deformations in shape space.

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“…Some of them are based on Graph Matching [25] or Game Theory [26]. Others perform the needed deformation by optimizing the parameters of Thin Plate Splines [27] or of a flexible surface regulated by a set of quadratic equations [28].…”

Section: Introductionmentioning

confidence: 99%

Information Theoretic Prototype Selection for Unattributed Graphs

Han

Rossi

Torsello

et al. 2012

Lecture Notes in Computer Science

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Abstract. Reducing the setup time for a new production line is critical to the success of a manufacturer within the current competitive and cost-conscious market. To this end, being able to reuse already available machines, toolings and parts is paramount. However, matching a large warehouse of previously engineered parts to a new component to produce, is often more a matter of art and personal expertise rather than predictable science. In order to ease this process we developed a database retrieval approach for mechanical components that is able to deal with both rigid matching and deformable shapes. The intended use for the system is to match parts acquired with a 3D scanning system to a large database of components and to supply a list of results sorted according with a metric that expresses a structural distance.

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Non-rigid Surface Registration Using Spherical Thin-Plate Splines

Cited by 31 publications

References 13 publications

Visualization of Shape Motions in Shape Space

Visualization of Shape Motions in Shape Space

Comparative Analysis of Quasi-Conformal Deformations in Shape Space

Information Theoretic Prototype Selection for Unattributed Graphs

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