A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

A Review of Point Feature Based Medical Image Registration

Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.

Interpolation Algorithm Research for Medical Image Registration

Owing to its property of applying multi-modality imaging information into the clinical usage has the Medical Image Registration been the research focus. The gray nearest neighbor interpolation and bilinear interpolation and cubic convolution interpolation method used to medical image interpolation algorithm. It compares the characteristics of the three gray interpolations. Combined with the characteristics of the above algorithm is proposed based on gray-scale pixel intensity interpolation. The algorithm can be improved in the time and registration accuracy. It combined with the improved optimization algorithm in the proposed image registration of the simulation experiment, experimental precision subpixel image to verify the validity of the method.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.

SeRN:A Two-Stage Framework of Registration for Semi-Supervised Learning for Medical Images

Significant breakthroughs in medical image registration have been achieved using deep neural networks(DNNs).However,DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training.To leverage the intensity information of abundant unlabeled images,unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However,finding a sufficiently robust measure can be challenging for specific registration applications.Weakly supervised registration methods use anatomical labels to estimate the deformation between images.High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images,whereas label images are extremely difficult to collect.In this paper,we propose a two-stage semi-supervised learning framework for medical image registration,which consists of unsupervised and weakly supervised registration networks.The proposed semi-supervised learning framework is trained with intensity information from available images,label information from a relatively small number of labeled images and pseudo-label information from unlabeled images.Experimental results on two datasets(cardiac and abdominal images)demonstrate the efficacy and efficiency of this method in intra-and inter-modality medical image registrations,as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available.Our code is publicly available at at https://github.com/jdq818/SeRN.

Rapid and robust medical image elastic registration using mean shift algorithm

In landmark-based image registration, estimating the landmark correspondence plays an important role. In this letter, a novel landmark correspondence estimation technique using mean shift algorithm is proposed. Image corner points are detected as landmarks and mean shift iterations are adopted to find the most probable corresponding point positions in two images. Mutual information between intensity of two local regions is computed to eliminate mis-matching points to improve the stability of corresponding estimation correspondence landmarks is exact. The proposed experiments of various mono-modal medical images. Multi-level estimation （MLE） technique is proposed Experiments show that the precision in location of technique is shown to be feasible and rapid in the

Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

Medical image registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondence between planning and treatment images.However,most methods based on intensity have the problems of matching ambiguity and ignoring the influence of weak correspondence areas on the overall registration.In this study,we propose a novel general-purpose registration algorithm based on free-form deformation by non-subsampled contourlet transform and saliency map,which can reduce the matching ambiguities and maintain the topological structure of weak correspondence areas.An optimization method based on Markov random fields is used to optimize the registration process.Experiments on four public datasets from brain,cardiac,and lung have demonstrated the general applicability and the accuracy of our algorithm compared with two state-of-the-art methods.