DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem

Brain Functional Localization: A Survey of Image Registration Techniques

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms' similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.

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Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Multimodal image registration is a class of algorithms to find correspondence from different modalities. Since different modalities do not exhibit the same characteristics, finding accurate correspondence still remains a challenge. To deal with this, mutual information (MI)-based registration has been a preferred choice as MI is based on the statistical relationship between both volumes to be registered. However, MI has some limitations. First, MI-based registration often fails when there are local intensity variations in the volumes. Second, MI only considers the statistical intensity relationships between both volumes and ignores the spatial and geometric information about the voxel. In this work, we propose to address these limitations by incorporating spatial and geometric information via a 3D Harris operator. In particular, we focus on the registration between a high-resolution image and a low-resolution image. The MI cost function is computed in the regions where there are large spatial variations such as corner or edge. In addition, the MI cost function is augmented with geometric information derived from the 3D Harris operator applied to the high-resolution image. The robustness and accuracy of the proposed method were demonstrated using experiments on synthetic and clinical data including the brain and the tongue. The proposed method provided accurate registration and yielded better performance over standard registration methods.

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Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context

Self-similarity weighted mutual information: a new nonrigid image registration metric.

https://www.cbica.upenn.edu/sbia/papers/103.pdf

Multimodality image registration by maximization of quantitative-qualitative measure of mutual information

This paper presents a novel measure of image similarity, called quantitative-qualitative measure of mutual information (Q-MI), for multi-modal image registration Conventional information measure, ie, Shannon’s entropy, is a quantitative measure of information, since it only considers probabilities, not utilities of events Actually, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence Therefore, it is important to consider both quantitative and qualitative (ie, utility) information simultaneously for image registration To achieve this, salient voxels such as white matter (WM) voxels near to brain cortex will be assigned higher utilities than the WM voxels inside the large WM regions, according to the regional saliency values calculated from scale-space map of brain image Thus, voxels with higher utilities will contribute more in measuring the mutual information of two images under registration We use this novel measure of mutual information (Q-MI) for registration of multi-modality brain images, and find that the successful rate of our registration method is much higher than that of conventional mutual information registration method

/pdf/multi-modal-image-registration-by-quantitative-qualitative-4cy92ja2r4.pdf

Hongxia Luan

Papers

Multimodality image registration by maximization of quantitative-qualitative measure of mutual information

Multi-modal image registration by quantitative-qualitative measure of mutual information (Q-MI)