Abstract
Linear registration is often the crucial first step for various types of image analysis. Although this is mathematically simple, failure is not uncommon. When investigating the brain by magnetic resonance imaging (MRI), the brain is the target organ for registration but the existence of other tissues, in addition to a variety of fields of view, different brain locations, orientations and anatomical features, poses some serious fundamental challenges. Consequently, a number of different algorithms have been put forward to minimize potential errors. In the present study, we tested a knowledge-based approach that can be combined with any form of registration algorithm. This approach consisted of a library of intermediate images (mediators) with known transformation to the target image. Test images were first registered to all mediators and the best mediator was selected to ensure optimum registration to the target. In order to select the best mediator, we evaluated two similarity criteria: the sum of squared differences and mutual information. This approach was applied to 48 mediators and 96 test images. In order to reduce one of the main drawbacks of the approach, increased computation time, we reduced the size of the library by clustering. Our results indicated clear improvement in registration accuracy.
Highlights
Image registration is a method used to align multiple images to ensure the spatial correspondence of anatomy across different images
The primary goal of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database is to assess whether serial magnetic resonance imaging (MRI), positron emission tomography, other biological markers, or clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment and early Alzheimer’s disease
Linear Registration With a Single Mediator We tested if a mediator with similar imaging parameter could improve the registration accuracy compared to the pairwise direct linear registration
Summary
Image registration is a method used to align multiple images to ensure the spatial correspondence of anatomy across different images. There are two types of registration algorithm, which are based on transformation models: linear and non-linear registration. Linear registration is used widely and predominantly involves six-parametric rigid transformation (rotation and translation on x, y, and z coordinate axes) or 12-parametric affine transformation (rotation, translation, scaling, and shearing on x, y, and z coordinate axes). The linear registration is global in nature while non-linear registration has a higher degree of elasticity which can model local deformation. Registration is an essential step for many types of medical image analysis including voxel-based analysis, change detection, cross-modality image fusion and image segmentation [for reviews see. The success of studies involving image analysis depends heavily upon registration accuracy. Linear registration is an essential first step for registration-based analysis, followed by local nonlinear registration. The quality of the initial linear registration is often crucial for subsequent steps
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