Related Experiment Video
Updated: May 21, 2026
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
Published on: October 27, 2023
MIND: modality independent neighbourhood descriptor for multi-modal deformable registration
Mattias P Heinrich1, Mark Jenkinson, Manav Bhushan
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. mattias.heinrich@eng.ox.ac.uk
Medical Image Analysis
|June 23, 2012
Summary
This study introduces a Modality Independent Neighbourhood Descriptor (MIND) for robust multi-modal medical image registration. MIND accurately aligns images from different sources, outperforming existing methods.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Computational Anatomy
Background:
- Multi-modal medical image registration is crucial but challenging due to inherent differences between imaging modalities.
- Existing methods struggle with variations in intensity, noise, and bias fields, limiting their effectiveness.
- Accurate image registration is vital for diagnosis, treatment planning, and disease monitoring.
Purpose of the Study:
- To propose and validate a novel Modality Independent Neighbourhood Descriptor (MIND) for linear and deformable multi-modal image registration.
- To demonstrate MIND's robustness to inter-modality differences, including intensity variations, noise, and bias fields.
- To evaluate MIND's performance against state-of-the-art techniques using clinical datasets.
Main Methods:
- Developed a descriptor based on local image patch self-similarity, capturing distinctive local structures preserved across modalities.
- MIND descriptor is robust to intensity differences, noise, and bias fields, enabling point-wise local similarity computation.
- Integrated MIND into a symmetric non-parametric Gauss-Newton registration framework using sum of squared differences as the similarity metric.
Main Results:
- MIND effectively extracts distinctive local image structures invariant to modality-specific characteristics.
- The descriptor demonstrated robustness against significant inter-modality differences and image degradations.
- Experiments on 3D thoracic CT (inhale/exhale) and CT-MRI alignment showed MIND's superiority over conditional mutual information and entropy images.
Conclusions:
- MIND offers a powerful and versatile approach for multi-modal medical image registration.
- The descriptor's robustness and efficiency make it suitable for various transformation models and optimization algorithms.
- MIND represents a significant advancement in medical image analysis, particularly for cross-modality alignment tasks.
Related Concept Videos
01:09Functional Classification of Joints
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
01:11Deformation of Member under Multiple Loadings
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
01:20Structural Classification of Joints
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...