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Related Experiment Video

Updated: May 7, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

The richer representation the better registration.

Mohammad Rouhani, Angel Domingo Sappa

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 18, 2013

Summary

This study introduces a novel point-to-model registration method that avoids time-consuming correspondence searches. By using implicit functions, it achieves robust and fast convergence for accurate 2D and 3D data registration.

Area of Science:

Computer Vision
Geometric Modeling
Scientific Computing

Background:

Traditional point-wise registration methods rely on time-consuming correspondence searches.
Implicit functions offer a powerful way to represent geometric models.

Purpose of the Study:

To reformulate the registration problem as a point-to-model distance minimization.
To develop a registration method that bypasses explicit correspondence matching.
To leverage implicit function representations for efficient registration.

Main Methods:

The target set is represented using implicit functions (polynomials or B-splines) via linear least squares fitting.
Point-to-point registration is converted into a point-to-implicit registration problem.
The registration distance is minimized using the Levengberg–Marquardt algorithm.

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Main Results:

The proposed method successfully avoids correspondence search, significantly improving speed.
The approach demonstrates robustness and fast convergence in both 2D and 3D experiments.
A trade-off between speed and accuracy can be achieved by selecting appropriate implicit functions.

Conclusions:

The developed point-to-model registration method is efficient, robust, and easy to implement.
Implicit function representations provide a flexible framework for geometric registration.
This approach offers a promising alternative to traditional correspondence-based registration techniques.