The procedure of mapping points from one Image to corresponding points in another Image is called Image Registration. It is a spatial transform. By the nature of the Image transformation, Image Registration can be classified into rigid Image Registration and non-rigid Image Registration. One approaches of non-rigid Image Registration is solving the Registration problem as an optimization problem. An example of these methods is the graph-cuts based Registration. The basic technique is to construct a specialized graph for the energy function to be minimized such that the minimum cut on the graph also minimizes the energy. Given that the time is one of the critical factors in medical applications, it seems that improvement of this method in terms of run time will be helpful for its clinical and medical applications. In order to achieve this goal, in this research we proposed a method that significantly reduces the run time of Registration process. The implementation results of our proposed method on the Images with artificial deformations shows that the proposed algorithm is about three times faster than the main algorithm, while the average amount of SAD criterion will be increased from 0.7 to 1 .

Accurate Registration of preoperative Magnetic Resonance Imaging (MRI) with intraoperative ultrasound (US) is essential for effective neuronavigation, particularly in Brain tumor surgeries where Brain shift compromises anatomical fidelity. This study proposes a hybrid framework integrating a deep learning-based Multi-Layer Perceptron (MLP) with an optimization pipeline to enhance MR-to-US Registration. The MLP is trained on paired anatomical landmarks extracted from the BITE and RESECT datasets to predict US coordinates from corresponding MRI points. An ensemble of five MLPs, weighted by inverse validation errors, is employed to estimate dense point correspondences, which are used to initialize an affine transformation. This transformation is refined using Symmetric Normalization (SyN) within the ANTs Registration toolkit to model non-linear deformations. Quantitative evaluation demonstrates a mean squared error (MSE) of 0.1954 and a mean Euclidean distance of 4.97 mm—significantly outperforming a baseline rigid Registration approach with 60% improvement in spatial alignment. The proposed pipeline executes in under 4 minutes per case on standard hardware, indicating potential for clinical integration. The results suggest combining learning-based correspondence prediction and classical Registration yields accurate and computationally efficient multimodal Registration.

Diffusion Tensor Imaging (DTI) is a common method for the investigation of Brain white matter. In this method, it is assumed that diffusion of water molecules is Gaussian and so, it fails in fiber crossings where this assumption does not hold. High Angular Resolution Diffusion Imaging (HARDI) allows more accurate investigation of microstructures of the Brain white matter; it can present fiber crossing in each voxel. HARDI contains complex orientation information of the fibers.Therefore, Registration of these Images is more complicated than the scalar Images. In this paper, we propose a HARDI Registration algorithm based on the feature vectors that are extracted from the Orientation Distribution Functions (ODFs) in each voxel. Hammer similarity measure is used to match the feature vectors and thin-plate spline (TPS) based Registration is used for spatial Registration of the skeleton and its neighbors. A re-orientation strategy is utilized to re-orient the ODFs after spatial Registration. Finally, we evaluate our method based on the differences in principal diffusion direction and we will show that utilizing the skeleton as landmark in the Registration results in accurate alignment of HARDI data.

Image Registration is a crucial step in most Image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of Registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally Image resembling. As the accuracy of a Registration process is highly dependent to the feature extraction and matching methods, in this paper, we have proposed a new method for extracting salient edges from satellite Images. Due to the efficiency of multiresolution data representation, we have considered four state-of-the-art multiresolution transforms -namely, wavelet, curvelet, complex wavelet and contourlet transform- in the feature extraction step of the proposed Image Registration method. Experimental results and performance comparison among these transformations showed the high performance of the contourlet transform in extracting efficient edges from satellite Images. Obtaining salient, stable and distinguishable features increased the accuracy of the proposed Registration process.

Targets and objects Registration and tracking in a sequence of Images play an important role in various areas. One of the methods in Image Registration is feature-based algorithm which is accomplished in two steps. The first step includes finding features of sensed and reference Images. In this step, a scale space is used to reduce the sensitivity of detected features to the scale changes. Afterward, we attribute feature points that obtained in the first step, descriptions using brightness value around the feature points. In this paper, a new algorithm is proposed based on Binary Robust Invariant Scalable Keypoints (BRISK) and Scale Invariant Feature Transform (SIFT) algorithms. The proposed algorithm uses the directional pattern to describe the edges which are around the keypoints. This pattern is perpendicular to the direction of keypoints which shows the direction of the edge and provides more useful information regarding brightness around the feature point to make descriptor vector. Furthermore, in the proposed algorithm, the output vector consists of multilevel values instead of binary values which means further useful information is involved in the descriptor vector. Also, levels of output vectors can be adjusted using a single parameter so that the processor with low computing ability can tune the output to a binary vector. Experimental results show that the proposed algorithm is more robust than the BRISK algorithm and the efficiency of the algorithm is about the same as BRISK algorithm.

Image Registration is one of the fields widely used in Image processing where much research has been done. Image Registration is thealignment and compliance of two or more Images from different imaging conditions. Itsapplications include change identification between Images, Image fusion, object recognition, and Image mosaic. In this paper, in addition to introducing the concepts of Image Registration, we have collected and classified different researches, as well as definition of the research approach thereof. Moreover, thevarious aspects of Image Registration have been evaluated through four different tests. This paper could pave the way for researchers in the field of Image processing as and it has been tried to includeall aspects of this field of study herein.

Although, most of the abnormal structures of human Brain do not alter the shape of outer envelope of Brain (surface), some abnormalities can deform the surface extensively. However, this may be a major problem in a surface-based Registration technique, since two nearly identical surfaces are required for surface fitting process. A type of verification known as the circularity check for the shape of the detected head contours was defined based on the curvature measurement. Any unacceptable deformity (or hole) existing in the Brain surface can be detected by the circularity check and reformed by a type of interpolation process. Two techniques were suggested to reform the abnormal regions and holes on the surfaces: one based on median filtering and another based on contour reflection

Image Registration aims to establish precise geometrically alignment between two Images of the same scene taken at different times، different viewpoints، or different sensors. It is an essential task in diverse remote sensing and photogrammetry processes such as change detection، 3D modeling، information fusion، geometric correction، and bundle block adjustment. Nowadays، feature based approaches are generally used for satellite Image Registration due to their resistant against geometric and radiometric variations. A feature based Image Registration method comprises three main steps: (1) control point (tie-point) extraction، (2) transformation model computation، and (3) Image resampling. In the first step، some distinctive conjugate features are automatically extracted using various Image matching approaches. In the second step، a suitable transformation model between two Images is computed using the extracted control points from the previous step. In the third step، the input Image is rectified to the geometry of the base Image using the computed transformation model. Transformation models generate the spatial relation between the Images and play a critical role in the positional accuracy of the Image Registration process. Various transformation models have been proposed for remote sensing Image Registration. The transformation models are generally divided into two types: (a) global models and (b) adaptive models. The global models have a constant number of parameters and describe the global spatial relations between two Images. In contrast، the number of parameters in adaptive models are not constant and varies with the severity of the geometric variations and the number of control points. Each transformation model has its strengths and drawbacks. In this paper the capability of some popular transformation models، including similarity، projective، polynomials of degrees 1 to 4، piecewise linear (PL)، Multiquaderic (MQ) and Pointwise (PW) are evaluated for high resolution stereo satellite Imagery. To extract high accurate and well distributed control points، an integrated Image matching process based on FAST (Features from Accelerated Segment Test) detector، SIFT (Scale Invariant Feature Transform) descriptor and the least square matching algorithm has been proposed. In this method، the initial point features are efficiently extracted using FAST algorithm in a gridding strategy. Then، the well-known SIFT descriptors are computed for extracted features. The control points are determined using feature descriptor comparison in two Images، and their positional accuracy are improved using least square matching method. Two evaluation criteria، including computation speed and positional accuracy are used to investigate the capability of the transformation models. To investigate the effect of the feature density in quality of the transformation models، the extracted control points are divided into four classes with different number and distribution of the control points. The experimental results using two high resolution remote sensing Image pairs from ZY3 and IKONOS sensors، show that the adaptive MQ model provides the best results، followed by PW and PL models. In contrast، the similarity، projective and global polynomial models do not provide acceptable results for accurate Registration in high resolution remote sensing Images. However، the commutation time in adaptive models especially in MQ is very high. The Registration accuracy of the proposed approach for the MQ model is 2 and 1. 9 pixel، for the ZY3 and IKONOS Images respectively

Small size multi-spectral cameras are generating recently due to the fast development in UAVs technology and their application in agricultural field. These cameras are designed based on multi lenses structure where each lens captures Images in special electromagnetic waves. There is a challenge in band to band co-Registration due to this multi lens structure. On the other hand, Low altitude flight is necessary for some applications like plant disease detection where sub-pixel accuracy is needed to detect small features. Therefore, there is a higher miss-Registration problem based on relief displacement in low flight altitude on top of trees after Registration. The purpose of this article is to reduce the relief displacement error by introducing the most efficient Image Registration method. Therefore, three different datasets with different altitude are applied for this purpose. Results showed the proposed method was successful %83 to reduce miss-Registration error.