Automatic characteristic-calibrated registration (ACC-REG): Hippocampal surface registration using eigen-graphs

Chan, Hei Long; Yam, Tsz Chun; Lui, Lok Ming

doi:10.1016/j.patcog.2019.107142

Cited by 8 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each f i represents the vibration mode at a specific frequency λ j where j represents the j -th frequency. Subsequently, the first eigenfunction f 1 according to the first nontrivial eigenvalue is defined as the eigen-graph of H ( 9 ). Because f 1 and λ 1 are dependent only on the Riemannian structure of the manifold, f 1 contains the intrinsic geometric information of H .…”

Section: Methodsmentioning

confidence: 99%

“…In addition, due to the lack of consideration of large variations between individuals, the extracted eigen-graphs between individuals lacked correspondences. Later, Chan et al ( 9 ) applied the calibration of the eigen-graphs to improve the accuracy of the eigen-graph correspondences. However, this method excessively pursued the smoothness of the landmark curves and neglected the intrinsic morphological features of hippocampal surfaces.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Li,

Su,

Yao

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background The cognitive decline induced by Alzheimer’s disease (AD) is closely related to changes in hippocampal structure captured by magnetic resonance imaging (MRI). To accurately analyze the morphological changes of the hippocampus induced by AD, it is necessary to establish a one-to-one surface correspondence to compare the morphological measurements across different hippocampal surfaces. However, most existing landmark-based registration methods cannot satisfy both landmark matching and diffeomorphism under large deformations. To address these challenges, we propose a landmark-based spherical registration method via quasi-conformal mapping to establish a one-to-one correspondence between different hippocampal surfaces. Methods In our approach, we use the eigen-graph of the hippocampal surface to extract the intrinsic and unified landmarks of all the hippocampal surfaces and then realize the parameterization process from the hippocampal surface to a unit sphere according to the barycentric coordinate theory and the triangular mesh optimization algorithm. Finally, through the local stereographic projection, the alignment of the landmarks is achieved based on the quasi-conformal mapping on a two-dimensional (2D) plane under the constraints of Beltrami coefficients which can effectively control the topology distortion. Results We verified the proposed registration method on real hippocampus data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and created AD and normal control (NC) groups. Our registration algorithm achieved an area distortion index (ADI) of 0.4362e−4±0.7800e−5 in the AD group and 0.5671e−4±0.602e−5 in the NC group, and it achieved an angle distortion index (E angle ) of 0.6407±0.0258 in the AD group and 0.6271±0.0194 in the NC group. The accuracy of support vector machine (SVM) classification for the AD vs. NC groups based on the morphological features extracted from the registered hippocampal surfaces reached 94.2%. Conclusions This landmark-based spherical quasi-conformal mapping for hippocampal surface registration algorithm can maintain precise alignment of the landmarks and bijectivity in the presence of large deformation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Li,

Su,

Yao

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…However, due to the lack of consideration of large variations between individuals, the extracted eigen-graphs between individuals lacked correspondences. Later, Hei et al [3] applied the calibration of the eigen-graphs to improve the accuracy of the eigen-graph correspondences. However, this method excessively pursued the smoothness of the landmark curves and neglected the intrinsic morphological features of the hippocampal surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the above analysis, we used PCA to extract the north and south poles (N and S) of each hippocampus, and we used Laplace Beltrami (LB) operator to extract two landmark curves that could stably describe the intrinsic morphological characteristics of the hippocampus based on [3]. Then we used the angle correction method of spatial geometry to correct the extracted landmark curves.…”

Section: Introductionmentioning

confidence: 99%

Landmark extraction for hippocampal surfaces

Li,

Wang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

The cognitive decline caused by Alzheimer's disease (AD) is closely related to changes in the hippocampal structure captured by magnetic resonance imaging (MRI). To accurately analyze the morphological changes in the hippocampus caused by AD, it is necessary to extract the landmarks on the hippocampal surfaces which can ensure accurate registration between the different hippocampal surfaces, to eliminate morphological measurement errors caused by factors such as image acquisition, age, and gender. In this paper, we propose a method that can automatically extract the intrinsic landmarks of the hippocampal surfaces. More specifically, we use the eigen-graph of the hippocampal surfaces to extract the intrinsic and unified landmarks of all the hippocampal surfaces and calibrate along the longitudinal direction of the hippocampus surfaces along with the landmark correction for a few irregular hippocampal surfaces. Finally, we apply the extracted landmarks to the landmark-based registration algorithm, and the results show that landmark-based registration using our extracted landmarks can improve the accuracy of AD classification.

show abstract

Recent Developments of Surface Parameterization Methods Using Quasi-conformal Geometry

Choi

Lui

2022

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

View full text Add to dashboard Cite

Automatic characteristic-calibrated registration (ACC-REG): Hippocampal surface registration using eigen-graphs

Cited by 8 publications

References 14 publications

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Landmark extraction for hippocampal surfaces

Recent Developments of Surface Parameterization Methods Using Quasi-conformal Geometry

Contact Info

Product

Resources

About