Medical Image Registration Based on Phase Congruency and Regional Mutual Information

In this paper, a new approach of muhi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, instead of standard mutual information （ MI ） based on joint intensity histogram, regional mutual information （ RMI ） is employed, which allows neighborhood information to be taken into account. Secondly, a new feature images obtained by means of phase congruency are invariants to brightness or contrast changes. By incorporating these features and intensity into RMI, we can combine the aspects of both structural and neighborhood information together, which offers a more robust and a high level of registration accuracy.

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.

Image Registration Based on Improved Mutual Information with Hybrid Optimizer

An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradient information is that locations a large gradient magnitude should be aligned, but also the orientation of the gradients at those locations should be similar. Secondly, a hybrid optimizer combined PSO with Powell algorithm is proposed to restrain local maxima of mutual information function and improve the registration accuracy to sub-pixel level. Lastly, muhlresolution data structure based on Mallat decomposition can not only improve the behavior of registration function, but also improve the speed of the algorithm. Experimental results demonstrate that the new method can yield good registration result, superior to traditional optimizer with respect to smoothness and attraction basin as well as convergence speed.

Robust Image Registration Based on Mutual Information Measure

A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.

Study of three-dimensional PET and MR image registration based on higher-order mutual information

Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information sometimes mightlead to misregistration because of neglecting spatial information and treating intensityvariations with undue sensitivity. In this paper, an extension of mutual informationframework was proposed in which higher-order spatial information regarding imagestructures was incorporated into the registration processing of PET and MR. Thesecond-order estimate of mutual information algorithm was applied to the registrationof seven patients. Evaluation from Vanderbilt University and our visual inspectionshowed that sub-voxel accuracy and robust results were achieved in all cases withsecond-order mutual information as the similarity measure and with Powell's multi-dimensional direction set method as optimization strategy.

Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

Object detection in unmanned aerial vehicle(UAV)aerial images has become increasingly important in military and civil applications.General object detection models are not robust enough against interclass similarity and intraclass variability of small objects,and UAV-specific nuisances such as uncontrolledweather conditions.Unlike previous approaches focusing on high-level semantic information,we report the importance of underlying features to improve detection accuracy and robustness fromthe information-theoretic perspective.Specifically,we propose a robust and discriminative feature learning approach through mutual information maximization(RD-MIM),which can be integrated into numerous object detection methods for aerial images.Firstly,we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain.Then,we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories.Finally,we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields.We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)datasets to prove the effectiveness of the proposed method.The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods,achieving relative growth rates of 51.0%and 39.4%in corruption robustness,respectively.Our code is available at https://github.com/cq100/RD-MIM(accessed on 2 August 2024).

Registration Method for CT-MR Image Based on Mutual Information

Medical image registration is important in many medical applications. Registration method based on maximization of mutual information of voxel intensities is one of the most popular methods for 3-D multi-modality medical image registration. Generally, the optimization process is easily trapped in local maximum, resulting in wrong registration results. In order to find the correct optimum, a new multi-resolution approach for brain image registration based on normalized mutual information is proposed. In this method, to eliminate the effect of local optima, multi-scale wavelet transformation is adopted to extract the image edge features. Then the feature images are registered, and the result at this level is taken as the initial estimate for the registration of the original images. Three-dimensional volumes are used to test the algorithm. Experimental results show that the registration strategy proposed is a robust and efficient method which can reach sub-voxel accuracy and improve the optimization speed.

Graphic Processing Unit-Accelerated Mutual Information-Based 3D Image Rigid Registration

Mutual information （MI）-based image registration is effective in registering medical images, but it is computationally expensive. This paper accelerates MI-based image registration by dividing computation of mutual information into spatial transformation and histogram-based calculation, and performing 3D spatial transformation and trilinear interpolation on graphic processing unit （GPU）. The 3D floating image is downloaded to GPU as flat 3D texture, and then fetched and interpolated for each new voxel location in fragment shader. The transformed resuits are rendered to textures by using frame buffer object （FBO） extension, and then read to the main memory used for the remaining computation on CPU. Experimental results show that GPU-accelerated method can achieve speedup about an order of magnitude with better registration result compared with the software implementation on a single-core CPU.

Fast Mutual Information Registration Method of 3-D Medical Image

Currently the voxel based registration methods have been used widely such as the well known mutual information (MI). Although the accuracy of their results is plausible, the registration procedure is slow. This paper proposed some methods to rigid registration based on mutual information, aiming for an acceleration of the registration process without significantly loss of precision in the final results. The efficiency of these methods is examined by registration of CT MR and PET MR. Experimental results show that the speedup is effective and efficient. By using the fast methods, the registration of 3 D medical image could also be implemented on PC rapidly.

k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

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.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.

Multimodality image registration and fusion using neural network

Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.

A new hybrid particle swarm optimization for multimodal brain image registration

Image registration is an important issue in medical analysis. In this process the spatial transformation that aligns the reference image and the floating image is estimated by optimizing a similarity metric. Mutual information (MI), a popular similarity metric, is a reliable criterion for medical image registration. In this paper, we present an improved method for multimodal image registration based on maximization of a new form of normalized MI incorporating particle swarm optimization, PSO, as a searching strategy. Also a new hybrid PSO algorithm is applied to approach more precise and robust results with better performance.

Using mutual information to evaluate performance of medical imaging systems

Information on physical image quality of medical images is important for imaging system assessment in order to promote and stimulate the development of state-of-the-art imaging systems. In this paper, we present a method for evaluating physical performance of medical imaging systems. In this method, mutual information (MI) which is a concept from information theory was used to measure combined properties of image noise and resolution of an imaging system. In our study, the MI was used as a measure to express the amount of information that an output image contains about an input object. The more the MI value provides, the better the image quality is. To validate the proposed method, computer simulations were per- formed to investigate the effects of noise and resolution degradation on the MI, followed by measuring and comparing the performance of two imaging systems. Our simulation and experimental results confirmed that the combined effect of deteriorated blur and noise on the images can be measured and analyzed using the MI metric. The results demonstrate the potential usefulness of the proposed method for evaluating physical quality of medical imaging systems.

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.

Mutual Information Optimization Based Dynamic Log-Polar Image Registration

Log-polar transformation(LPT)is widely used in image registration due to its scale and rotation invariant properties.Through LPT,rotation and scale transformation can be made into translation displacement in log-polar coordinates,and phase correlation technique can be used to get the displacement.In LPT based image registration,constant samples in digitalization processing produce less precise and effective results.Thus,dynamic log-polar transformation(DLPT)is used in this paper.DLPT is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical approach.Therefore,DLPT can get more precise and effective transformation results than the conventional LPT.Mutual information(MI)is a similarity measure to align two images and has been used in image registration for a long time.An optimal transform for image registration can be obtained by maximizing MI between the two images.Image registration based on MI is robust in noisy,occlusion and illumination changing circumstance.In this paper,we study image registration using MI and DLPT.Experiments with digitalizing images and with real image datasets are performed,and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.

Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.

Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging： observation, modeling, and verification

When using the image mutual information to assess the quality of reconstructed image in pseudothermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model.

Unsupervised segmentation of medical image based on difference of mutual information

In the scope of medical image processing, segmentation is important and difficult. There are still two problems which trouble us in this field. One is how to determine the number of clusters in an image and the other is how to segment medical images containing lesions. A new segmentation method called DDC, based on difference of mutual information （dMI） and pixon, is proposed in this paper. Experiments demonstrate that dMI shows one kind of intrinsic relationship between the segmented image and the original one and so it can be used to well determine the number of clusters. Furthermore, multi-modality medical images with lesions can be automatically and successfully segmented by DDC method.