Automatic Image Registration Method Research Articles

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.

Automatic intra-subject registration and fusion of multimodal cochlea 3D clinical images.

BackgroundThe postoperative imaging assessment of Cochlear Implant (CI) patients is imperative. The main obstacle is that Magnetic Resonance imaging (MR) is contraindicated or hindered by significant artefacts in most cases with CIs. This study describes an automatic cochlear image registration and fusion method that aims to help radiologists and surgeons to process pre-and postoperative 3D multimodal imaging studies in cochlear implant (CI) patients.Methods and findingsWe propose a new registration method, Automatic Cochlea Image Registration (ACIR-v3), which uses a stochastic quasi-Newton optimiser with a mutual information metric to find 3D rigid transform parameters for registration of preoperative and postoperative CI imaging. The method was tested against a clinical cochlear imaging dataset that contains 131 multimodal 3D imaging studies of 41 CI patients with preoperative and postoperative images. The preoperative images were MR, Multidetector Computed Tomography (MDCT) or Cone Beam Computed Tomography (CBCT) while the postoperative were CBCT. The average root mean squared error of ACIR-v3 method was 0.41 mm with a standard deviation of 0.39 mm. The results were evaluated quantitatively using the mean squared error of two 3D landmarks located manually by two neuroradiology experts in each image and compared to other previously known registration methods, e.g. Fast Preconditioner Stochastic Gradient Descent, in terms of accuracy and speed.ConclusionsOur method, ACIR-v3, produces high resolution images in the postoperative stage and allows for visualisation of the accurate anatomical details of the MRI with the absence of significant metallic artefacts. The method is implemented as an open-source plugin for 3D Slicer tool.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

The use of multiple dyes during histological sample preparation can reveal distinct tissue properties. However, since the slide preparation differs for each dye, the tissue slides are being deformed and a nonrigid registration is required before further processing. The registration of histology images is complicated because of: (i) a high resolution of histology images, (ii) complex, large, nonrigid deformations, (iii) difference in the appearance and partially missing data due to the use of multiple dyes. In this work, we propose a multistep, automatic, nonrigid image registration method dedicated to histology samples acquired with multiple stains. The proposed method consists of a feature-based affine registration, an exhaustive rotation alignment, an iterative, intensity-based affine registration, and a nonrigid alignment based on modality independent neighbourhood descriptor coupled with the Demons algorithm. A dedicated failure detection mechanism is proposed to make the method fully automatic, without the necessity of any manual interaction. The described method was proposed by the AGH team during the Automatic Non-rigid Histological Image Registration (ANHIR) challenge. The ANHIR dataset consists of 481 image pairs annotated by histology experts. Moreover, the ANHIR challenge submissions were evaluated using an independent, server-side evaluation tool. The main evaluation criteria was the target registration error normalized by the image diagonal. The median of median target registration error is below 0.19%. The proposed method is currently the second-best in terms of the average ranking of median target registration error, without statistically significant differences compared to the top-ranked method. We provide an open access to the method software and used parameters, making the results fully reproducible.

Automatic 3D image registration for nano-resolution chemical mapping using synchrotron spectro-tomography

Nano-resolution synchrotron X-ray spectro-tomography has been demonstrated as a powerful tool for probing the three-dimensional (3D) structural and chemical heterogeneity of a sample. By reconstructing a number of tomographic data sets recorded at different X-ray energy levels, the energy-dependent intensity variation in every given voxel fingerprints the corresponding local chemistry. The resolution and accuracy of this method, however, could be jeopardized by non-ideal experimental conditions, e.g. instability in the hardware system and/or in the sample itself. Herein is presented one such case, in which unanticipated sample deformation severely degrades the data quality. To address this issue, an automatic 3D image registration method is implemented to evaluate and correct this effect. The method allows the redox heterogeneity in partially delithiated LixTa0.3Mn0.4O2 battery cathode particles to be revealed with significantly improved fidelity.

An Automatic Image Registration Methods for Multi-Domain Images by using Geometric Relationships

In the internet technology world, all the information is considered as an image format only. Because one image is equal to a thousand words. Automatically increasing the image database size, high resolution and including multi-domain concepts. The human perception of getting the required image is very poor during the image retrieval process. In this case, the image retrieval system is consists of many problems like more time consuming, irrelevant results of the user. Registration is necessary to be able to compare or integrate the data obtained from different measurements of the query image. Image registration is that the method of reworking totally different sets of knowledge into one organization and orienting 2 or additional pictures of constant scene. This method involves designating one image because the reference image, conjointly known as the mounted image, and applying geometric transformations or native displacements to the opposite pictures so they align with the reference. To solve the above problems by using an automatic image registration methods. In this paper, we are mainly focusing the automatic image registration methods for multi-domain images for better human understating of required results. Finally, evaluate the performance of image registration methods with length, counts of matched points and uniformly distributed points of multi-domain images for better understanding.

Active contouring and 3D model deformable registration of radiotherapy planning and cone-beam computed tomography images

Background: To ensure high accuracy during radiation therapy (RT), the image-guided RT (IGRT) technique uses on-board cone-beam computed tomography (CBCT) scanning as an image guidance procedure for target localization before and during treatment. Adaptive RT aiming to modify RT target volumes according to kinetic changes in tumor shape during RT course is based on registration of CBCT and planning CT images. However, the re-contouring and re-planning procedures are extensively time and cost consuming. We developed a novel automatic contouring and image registration method to replace the manual re-contouring with accurate image registration. Methods: For the image sets with format of Digital Imaging and Communications in Medicine (DICOM) standard, we wrote a program in MATLAB language (Version R2016a) to read and convert CBCT images into cross-sectional (tomographic) images similar to those obtained via planning CT. For image enhancement, the active contouring by using Chan-Vese model with level set formulation was applied. To overcome the variations in spatial location of these two sets of CT images, the iterative closest point (ICP) algorithm was used for 3D model registration. The deformable image registration (DIR) with Double force Demons algorithm was performed for auto-transformation of contours from planning CT to CBCT images. Results: The customized program accurately converted the format of CBCT to planning CT. Image enhancement was achieved by our modified active contour model which solved the energy minimization problem. In 3D model registration, the variations in spatial location of the CBCT and planning CT images were corrected. After selection of most similar images, the planning CT images were registered to corresponding CBCT images. The registered images were clearer than CBCT images with removal of other confounding structures outside body contours. Conclusions: The planning CT and CBCT images could be precisely registered by using a novel established technique consisting of active contouring with 3D model and DIR. This technique would enable the on-line radiation treatment planning for adaptive radiotherapy.

High-Resolution Remote-Sensing Image Registration Based on Angle Matching of Edge Point Features

Remote-sensing image registration is an important prerequisite for many remote-sensing applications. The accuracy, efficiency, and automatic degree of registration have direct impacts on follow-up applications. With the improving of remote-sensing resolution, the size and the data amount of remote-sensing image are constantly increasing. Meanwhile, with the development of remote-sensing application, the performance of registration is also put forward increasing requirement. Therefore, this paper proposes an automatic and fast image registration method based on the angle matching of edge point features (EPFs). First, the original image is transformed by the Haar wavelet to get the approximate image to improve the registration speed. Second, edges in different source images are extracted by different edge detectors. Then, edge line features are transformed into EPFs to get accurate positioning and stable features. Third, the initial matching point pairs (MPPs) are determined by the ratio—the minimum angle to the second minimum angle between feature vectors of EPFs—which is less than a threshold. Fourth, in order to enhance the registration accuracy, the random sample consensus is improved by adding the quality constraint condition to delete error MPPs. Finally, the block thought is used to uniformly choose MPPs to calculate the affine transform parameters, which avoids the local optimal problem and further improves the registration accuracy. Multiple high-resolution remote-sensing datasets of registration experimental results indicate that the proposed method is fast implemented and has high accuracy and effectiveness in processing efficiency.

Ultrasound fusion image error correction using subject-specific liver motion model and automatic image registration

PurposeUltrasound fusion imaging is an emerging tool and benefits a variety of clinical applications, such as image-guided diagnosis and treatment of hepatocellular carcinoma and unresectable liver metastases. However, respiratory liver motion-induced misalignment of multimodal images (i.e., fusion error) compromises the effectiveness and practicability of this method. The purpose of this paper is to develop a subject-specific liver motion model and automatic registration-based method to correct the fusion error. MethodsAn online-built subject-specific motion model and automatic image registration method for 2D ultrasound-3D magnetic resonance (MR) images were combined to compensate for the respiratory liver motion. The key steps included: 1) Build a subject-specific liver motion model for current subject online and perform the initial registration of pre-acquired 3D MR and intra-operative ultrasound images; 2) During fusion imaging, compensate for liver motion first using the motion model, and then using an automatic registration method to further correct the respiratory fusion error. Evaluation experiments were conducted on liver phantom and five subjects. ResultsIn the phantom study, the fusion error (superior-inferior axis) was reduced from 13.90±2.38mm to 4.26±0.78mm by using the motion model only. The fusion error further decreased to 0.63±0.53mm by using the registration method. The registration method also decreased the rotation error from 7.06±0.21° to 1.18±0.66°. In the clinical study, the fusion error was reduced from 12.90±9.58mm to 6.12±2.90mm by using the motion model alone. Moreover, the fusion error decreased to 1.96±0.33mm by using the registration method. ConclusionsThe proposed method can effectively correct the respiration-induced fusion error to improve the fusion image quality. This method can also reduce the error correction dependency on the initial registration of ultrasound and MR images. Overall, the proposed method can improve the clinical practicability of ultrasound fusion imaging.

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation- and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.

Making People's Life Much Easier by Effective Face Image Retrieval from the Popular Online Social Network such as Facebook

The primary objective of this paper is to make people's life much easier by retrieving images containing faces of the same person appearing in the query image, from the popular online social network like Facebook (web-scale image database) containing millions of face images effectively and accurately. In this work face image of a person will be used as a query image to retrieve images of the same person with various poses, expressions, and illumination. Such face retrieval system has many applications, including name-based face image search, face tagging in images and videos, copyright enforcement, labeling faces in photos from the popular online social network such as Facebook etc. Also, this paper proposes a method for Automatic Image Registration in which pair of images with differences in rotation, translation, and spectral content is registered for remote sensing applications using multisensors. This work identifies that an image edge feature extraction using perceptional organization methods based on the rules of human perception are allowed us to reduce noise and over segmentation problem in image feature extraction. Generic edge tokens are perceptually significant edge features which are descriptive in the representation and therefore can be manipulated qualitatively in supporting perceptional grouping. This paper provided a new technique that can speed up registration (by reducing data) and improve accuracy by selecting a subimage for the registration process based on its ability to provide accurate results and determined a faster method for implementing multi-resolution image registration. The proposed work not only reduces the computation cost of future work, but it also greatly reduces the complexity and confusion caused by over-segmentation.

Distance-Dependent Multimodal Image Registration for Agriculture Tasks.

Image registration is the process of aligning two or more images of the same scene taken at different times; from different viewpoints; and/or by different sensors. This research focuses on developing a practical method for automatic image registration for agricultural systems that use multimodal sensory systems and operate in natural environments. While not limited to any particular modalities; here we focus on systems with visual and thermal sensory inputs. Our approach is based on pre-calibrating a distance-dependent transformation matrix (DDTM) between the sensors; and representing it in a compact way by regressing the distance-dependent coefficients as distance-dependent functions. The DDTM is measured by calculating a projective transformation matrix for varying distances between the sensors and possible targets. To do so we designed a unique experimental setup including unique Artificial Control Points (ACPs) and their detection algorithms for the two sensors. We demonstrate the utility of our approach using different experiments and evaluation criteria.

An automatic optical and SAR image registration method with iterative level set segmentation and SIFT

Although optical image registration methods have been successfully developed over the past decades, the registration of optical and synthetic aperture radar (SAR) images is still a challenging problem in remote sensing. Feature-based methods are considered to be more effective for multi-source image registration. However, almost all of these methods rely on the feature extraction algorithms. In this article, a simultaneous segmentation and feature-based registration method based on an iterative level set and scale-invariant feature transform (ILS-SIFT) is proposed. The core idea consists of three aspects: (1) an iterative procedure that combines image segmentation and matching is proposed to avoid registration failure caused by poor feature extraction; (2) a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features; and (3) an improved SIFT algorithm is employed to determine whether the registration was successful. Experimental results have shown the effectiveness and universality of the proposed method.

Medical image registration based on watershed transform from greyscale marker and multi-scale parameter search

We propose an automatic 3D medical image registration method that combines the watershed transform from greyscale marker, to effectively reduce the number of key points needed for registration, with a robust optimisation algorithm, called multi-scale parameter search (MSPS), to quickly estimate the mapping function. We evaluate it for rigid and intra-subject registration of pre- and post-surgery MR-T1 images of the brain. The visual analysis of its effectiveness is facilitated by a colour-coding scheme. Extensive experiments show that our approach is very accurate, robust to noise and provides 3D registration in less than 40 s, with no multi-resolution image schemes needed. We also evaluate MSPS on a testbed of 12 optimisation benchmark problems, in comparison with well-known optimisers, such as particle swarm optimiser, simulated annealing and differential evolution, showing that it can also be explored in other applications.

Robust image registration of biological microscopic images.

Image registration of biological data is challenging as complex deformation problems are common. Possible deformation effects can be caused in individual data preparation processes, involving morphological deformations, stain variations, stain artifacts, rotation, translation, and missing tissues. The combining deformation effects tend to make existing automatic registration methods perform poor. In our experiments on serial histopathological images, the six state of the art image registration techniques, including TrakEM2, SURF + affine transformation, UnwarpJ, bUnwarpJ, CLAHE + bUnwarpJ and BrainAligner, achieve no greater than 70% averaged accuracies, while the proposed method achieves 91.49% averaged accuracy. The proposed method has also been demonstrated to be significantly better in alignment of laser scanning microscope brain images and serial ssTEM images than the benchmark automatic approaches (p < 0.001). The contribution of this study is to introduce a fully automatic, robust and fast image registration method for 2D image registration.

Multi-contour registration based on feature points correspondence and two-stage gene expression programming

Abstract Image registration is a fundamental task in 3D reconstruction from an image sequence. Although this topic has been studied for decades, a general, robust, and automatic image registration method is rare, and most existing image registration methods are designed for a particular application. In this paper, image registration is treated as a formula discovery problem. A novel contour registration pipeline was proposed based on a foot-point-based feature point correspondence algorithm and a two-stage evolutionary algorithm. Our proposal has three objectives. First, we introduce a novel feature point extraction method that uses estimation of the curvature and the support region for every contour in the floating image. Second, we approximate the reference contour using a Gaussian mixture model (GMM) continuous optimization algorithm followed by an order-preserved foot-point detection method used to extract the feature points that correspond to the feature points of the floating contours. Third, we propose a hybrid evolutionary algorithm used to identify the registration formula for the reference image and the floating image. The hybrid evolutionary algorithm is a two-stage algorithm based on gene expression programming (GEP) and the improved cooperative particle swarm optimizer (CPSO). The optimal or near-optimal structure is accomplished using the GEP algorithm, and the parameters embedded in the structure are optimized by an opposition based learning (OBL)-based cooperative particle swarm optimizer (CPSO). Compared with other non-rigid registration methods, the developed registration pipeline produces competitive results with high accuracy.

Automated registration of multispectral MR vessel wall images of the carotid artery

Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA∕stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and moving image after registration. The average required manual translation per image slice was 1.33 mm. Translations were larger as the patient was longer inside the scanner. Manual alignment took 187.5 s per patient resulting in a mean surface distance of 0.271 ± 0.127 mm. After minimal user interaction to generate the mask in the fixed image, the remaining sequences are automatically registered with a computation time of 52.0 s per patient. The optimal registration strategy used a circular mask with a diameter of 10 mm, a 3D B-spline transformation model with a control point spacing of 15 mm, mutual information as image similarity metric, and the precontrast T1W TSE as fixed image. A mean surface distance of 0.288 ± 0.128 mm was obtained with these settings, which is very close to the accuracy of the manual alignment procedure. The exact registration parameters and software were made publicly available. An automated registration method was developed and optimized, only needing two mouse clicks to mark the start and end point of the artery. Validation on a large group of patients showed that automated image registration has similar accuracy as the manual alignment procedure, substantially reducing the amount of user interactions needed, and is multiple times faster. In conclusion, the authors believe that the proposed automated method can replace the current manual procedure, thereby reducing the time to analyze the images.

CHAIR: automatic image registration based on correlation and Hough transform

Automatic image registration is a process related to several application fields: remote sensing, medicine and computer vision, among others. Particularly in the field of remote sensing, the ever-increasing number of available satellite images requires automatic image registration methods, capable of correctly aligning a new image. An automatic image registration method – CHAIR (correlation- and Hough transform-based method of automatic image registration) – is proposed, the key concept of which relies on the ‘correlation image’ produced in both the horizontal and vertical directions. In particular, the computation of the distance of an identified diagonal brighter strip in the correlation image (through the Hough transform) to an offset (the main diagonal) allows for the determination of translational shifts and consequently control points. The set of obtained control points allows for the correction of several types of distortions. The geometric correction quality achieved by CHAIR was objectively evaluated through measures recently proposed, which allow for a more complete assessment of the obtained results. The CHAIR performance was evaluated on both synthetic and real data, with different spatial resolutions and spectral contents. CHAIR has been shown to be able to correctly align two images with a subpixel accuracy, having a priori a ‘gold standard’ image covering a considerable part of the image to be registered, and has also been shown to work for images of different sensors and/or different spectral bands, situations where traditional correlation methods often yield low and smooth peaks on the correlation surface. It is also able to account for elevation differences and to some extent for rotation and scale effects. Furthermore, it has been shown to have potential for registering synthetic aperture radar (SAR) with optical images.

Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.

A hybrid approach based on MEP and CSP for contour registration

Image registration is a fundamental task in 3D reconstruction from an image sequence. Although has been studied for decades, it is still rare to find a general, robust and automatic image registration method, and most existing image registration methods are designed for particular application. In this paper, image registration is boil down to a formula discovery problem to match feature points, we develop a new feature-based algorithm for contour registration automatically based on a hybrid approach combining Multi Expression Programming (MEP) with Clonal Selection Principle (CSP). Firstly, the image contours are extracted by fast global minimization of the active contour model, the feature point pairs which are used to establish training set of the hybrid approach are obtained using invariable moments. Secondly, the registration equations are acquired automatically by the proposed hybrid approach and the contours are then registered based on the equations. Experiments show that the proposed approach can be successfully applied to register the image pairs in which the contour information is well preserved.