The Experts below are selected from a list of 2265 Experts worldwide ranked by ideXlab platform
Xianfeng Gu - One of the best experts on this subject based on the ideXlab platform.
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MBIA/MFCA@MICCAI - Surface Foliation Based Brain Morphometry Analysis
Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, 2019Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, Xianfeng GuAbstract:Brain Morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
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Optimal mass transport based Brain Morphometry for patients with congenital hand deformities
The Visual Computer, 2018Co-Authors: Ming Ma, Xu Wang, Ye Duan, Scott H. Frey, Xianfeng GuAbstract:Congenital hand deformities (CHD) have attracted increasing research attention in the past few decades. The impacts of CHD on the Brain structure, however, are not fully studied to date. In this work, we propose a novel framework to study Brain Morphometry in CHD patients using Wasserstein distance based on optimal mass transport (OMT) theory. We first employ conformal mapping to map the left and right surface-based functional Brain areas to planar rectangles, which pushes the area element on the Brain surface to the planar rectangle and incurs the area distortion. A measure is then determined by this area distortion. We further propose a new rectangle domain-based OMT map algorithm. Given two measures on two surfaces, we employ the proposed algorithm to compute a unique OMT map between the two measures encoding the geometric information of left and right surface-based functional Brain areas. The transportation cost of this OMT map gives the Wasserstein distance between two surfaces, which intrinsically measures the dissimilarities between two surface-based shapes. Our method is theoretically rigorous and computationally efficient and stable. We finally evaluate the proposed Wasserstein distance-based method on the left and right post-central gyri from the CHD patients and healthy control subjects for analyzing Brain cortical Morphometry. Experimental results demonstrate the efficiency and efficacy of our method, and shed insightful lights on the study of the Brain Morphometry for those subjects with CHD.
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IPMI - Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.
Information processing in medical imaging : proceedings of the ... conference, 2015Co-Authors: Zhengyu Su, Wei Zeng, Yalin Wang, Zhong-lin Lu, Xianfeng GuAbstract:Brain Morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for Brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory.
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Brain Morphometry on congenital hand deformities based on Teichmüller space theory
Computer-aided Design, 2014Co-Authors: Hao Peng, Ye Duan, Xu Wang, Scott H. Frey, Xianfeng GuAbstract:Congenital Hand Deformities (CHD) usually occurred between the fourth and the eighth week after the embryo is formed. Failure of the transformation from arm bud cells to upper limb can lead to an abnormal appearing/functioning upper extremity which is presented at birth. Some causes are linked to genetics while others are affected by the environment, and the rest have remained unknown. CHD patients develop prehension through the use of their hands, which affects the Brain as time passes. In recent years, CHD have gained increasing attention and researches have been conducted on CHD, both surgically and psychologically. However, the impacts of CHD on the Brain structure are not well-understood so far. Here, we propose a novel approach to apply Teichmuller space theory and conformal welding method to study Brain Morphometry in CHD patients. Conformal welding signature reflects the geometric relations among different functional areas on the cortex surface, which is intrinsic to the Riemannian metric, invariant under conformal deformation, and encodes complete information of the functional area boundaries. The computational algorithm is based on discrete surface Ricci flow, which has theoretic guarantees for the existence and uniqueness of the solutions. In practice, discrete Ricci flow is equivalent to a convex optimization problem, therefore has high numerically stability. In this paper, we compute the signatures of contours on general 3D surfaces with the surface Ricci flow method, which encodes both global and local surface contour information. Then we evaluated the signatures of pre-central and post-central gyrus on healthy control and CHD subjects for analyzing Brain cortical Morphometry. Preliminary experimental results from 3D MRI data of CHD/control data demonstrate the effectiveness of our method. The statistical comparison between left and right Brain gives us a better understanding on Brain Morphometry of subjects with Congenital Hand Deformities, in particular, missing the distal part of the upper limb. We propose a novel Teichmuller space theory approach to study Brain Morphometry.Conformal welding signature reflects the geometric relations of different regions.The invertible method encodes complete information of the functional area boundaries.We evaluate signatures of pre-central and post-central gyrus on subjects and control.Congenital Hand Deformities may make a greater impact on post-central gyrus.
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teichmuller shape space theory and its application to Brain Morphometry
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
Yalin Wang - One of the best experts on this subject based on the ideXlab platform.
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MBIA/MFCA@MICCAI - Surface Foliation Based Brain Morphometry Analysis
Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, 2019Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, Xianfeng GuAbstract:Brain Morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
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Brain Morphometry Analysis with Surface Foliation Theory
arXiv: Computational Geometry, 2018Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, David Xianfeng GuAbstract:Brain Morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
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Conformal invariants for multiply connected surfaces: Application to landmark curve-based Brain Morphometry analysis
Medical Image Analysis, 2016Co-Authors: Wen Zhang, Miao Tang, Richard J. Caselli, Yalin WangAbstract:Abstract Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified Morphometry analysis. However, most of the landmark based Morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincare disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincare Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect Brain surface abnormalities. We also applied the new shape indices to analyze Brain Morphometry abnormalities associated with Alzheimer’ s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer’ s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling’ s T2 test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our Brain imaging analysis toolset for studying diagnosis and prognosis of AD.
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IPMI - Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.
Information processing in medical imaging : proceedings of the ... conference, 2015Co-Authors: Zhengyu Su, Wei Zeng, Yalin Wang, Zhong-lin Lu, Xianfeng GuAbstract:Brain Morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for Brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory.
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teichmuller shape space theory and its application to Brain Morphometry
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
Paul M Thompson - One of the best experts on this subject based on the ideXlab platform.
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New approaches in Brain Morphometry.
American Journal of Geriatric Psychiatry, 2013Co-Authors: Arthur W Toga, Paul M ThompsonAbstract:The complexity and variability of the human Brain across subjects is so great that reliance on maps and atlases is essential to effectively manipulate, analyze, and interpret Brain data. Central to these tasks is the construction of averages, templates, and models to describe how the Brain and its component parts are organized. Design of appropriate reference systems for human Brain data presents considerable challenges because these systems must capture how Brain structure and function vary in large populations, across age and gender, in different disease states, across imaging modalities, and even across species. The authors introduce the topic of Brain maps as applied to a variety of questions and problems in health and disease and include a brief survey of the types of maps relevant to mental disorders, including maps that capture dynamic patterns of Brain change in dementia.
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teichmuller shape space theory and its application to Brain Morphometry
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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MICCAI (1) - Teichmüller Shape Space Theory and Its Application to Brain Morphometry
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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ICCV - Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmuller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmuller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincare disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmuller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
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Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmüller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmüller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincaré disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmüller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmüller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
Arthur W Toga - One of the best experts on this subject based on the ideXlab platform.
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New approaches in Brain Morphometry.
American Journal of Geriatric Psychiatry, 2013Co-Authors: Arthur W Toga, Paul M ThompsonAbstract:The complexity and variability of the human Brain across subjects is so great that reliance on maps and atlases is essential to effectively manipulate, analyze, and interpret Brain data. Central to these tasks is the construction of averages, templates, and models to describe how the Brain and its component parts are organized. Design of appropriate reference systems for human Brain data presents considerable challenges because these systems must capture how Brain structure and function vary in large populations, across age and gender, in different disease states, across imaging modalities, and even across species. The authors introduce the topic of Brain maps as applied to a variety of questions and problems in health and disease and include a brief survey of the types of maps relevant to mental disorders, including maps that capture dynamic patterns of Brain change in dementia.
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teichmuller shape space theory and its application to Brain Morphometry
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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MICCAI (1) - Teichmüller Shape Space Theory and Its Application to Brain Morphometry
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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ICCV - Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmuller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmuller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincare disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmuller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
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Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmüller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmüller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincaré disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmüller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmüller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
Tony F Chan - One of the best experts on this subject based on the ideXlab platform.
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teichmuller shape space theory and its application to Brain Morphometry
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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MICCAI (1) - Teichmüller Shape Space Theory and Its Application to Brain Morphometry
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M ThompsonAbstract:Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.
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ICCV - Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmuller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmuller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincare disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmuller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
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Studying Brain Morphometry using conformal equivalence class
2009 IEEE 12th International Conference on Computer Vision, 2009Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Yi-yu Chou, Paul M ThompsonAbstract:Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmüller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study Brain Morphometry. The shape index is defined based on Teichmüller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincaré disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical Morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmüller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmüller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface Morphometry.
