Intensity-based Image Registration Methods Research Articles

Development of panoramic infrared images for surface temperature analysis of buildings and infrastructures

The surface temperature distribution of buildings and infrastructures is a key piece of information in the analysis of built environments especially in urban areas. It not only helps understand and model the thermal performance of buildings, but also reveal the thermal environments exposed by pedestrians. The information may further help improve the energy efficiency of buildings and reduce the urban heat island (UHI) effect. However, effective methods are currently lacking to visualize and analyze the overall surface temperature of buildings and urban infrastructures in a large scale. To cover this gap, a systematic approach is developed in this study to generate panoramic infrared images. The approach includes: (1) use of a ground-based imaging system to capture visual and infrared images, (2) development of spatial transformation models between the visual and infrared images using intensity-based image registration method, (3) creation of the panoramic visual image and fusion of the panoramic visual and infrared images, (4) geometrical transformation of the panoramic images with a modified cylindrical projection method. Three cases are used to illustrate the potential applications of the approach in the analysis of building surfaces. The cases indicate that the methods can effectively reveal the temporal and spatial temperature variations of large building surfaces, providing insights on the effects of cladding materials on surface temperature.

Approach of spectral information-based image registration similarity

For local intensity-based image registration methods, a template of a predefined size is adopted. Some measure of similarity between the images is used to determine when the optimal alignment has occurred. Most intensity-based similarity utilizes the statistical and spatial information of an image. However, there are significant nonlinear radiometric differences between infrared and visible data. Images of natural scenes usually do not have enough spatial features within the template. Hence, many similarities will be useless when dealing with infrared and visible data. Minor eigenvalues (ME) image registration similarity is presented with the exploitation the spectral properties of remote sensing images. ME similarity is based on the linear spectral mixture model and detects control points through searching the minimum of ME of the covariance matrix. Experiments on Landsat-7 satellite Enhanced Thematic Mapper Plus data are performed to verify and evaluate the effectiveness. Transformation performance curves, correct match ratio (CMR), and registration accuracy are also discussed. According to the data, root-mean-square error of phase correlations is 0.0722 pixels and the CMR of ME similarity is nearly 100%. The results on the basis of TM1, TM2, TM3, and TM4 band images indicate that the proposed similarity holds promise for infrared and color image registration in natural scenes, with advantages over previous normal mutual information and gradient mutual information similarities.

ARENA: Inter-modality affine registration using evolutionary strategy.

Image fusion of different imaging modalities renders valuable information to clinicians. In this paper, we proposed an automatic multimodal registration method to register intra-operative ultrasound images (US) to preoperative magnetic resonance images (MRI) in the context of image-guided neurosurgery. We employed refined correlation ratio as a similarity metric for our intensity-based image registration method. We deem MRI as the fixed image ([Formula: see text]) and US as the moving image ([Formula: see text]) and then transform [Formula: see text] to align with [Formula: see text]. We utilized the covariance matrix adaptation evolutionary strategy to find the optimal affine transformation in registration of [Formula: see text] to [Formula: see text]. We applied our method on the publicly available retrospective evaluation of cerebral tumors (RESECT) database and Montreal Neurological Institute's brain images of tumors for evaluation (BITE) database. We validated the results qualitatively and quantitatively. Qualitative validation is conducted (by the three authors) through overlaying pre- and post-registration US and MRI to allow visual assessment of the alignment. Quantitative validation is performed by utilizing the corresponding landmarks in the databases for the preoperative MRI and the intra-operative US. Average mean target registration error (mTRE) has been reduced from [Formula: see text] to [Formula: see text] in 22 patients in the RESECT database and from [Formula: see text] to [Formula: see text] in the BITE database. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a p value of [Formula: see text] for the RESECT database and [Formula: see text] for the BITE database. The proposed fully automatic registration method significantly improved the alignment of MRI and US images and can therefore be used to reduce the misalignment of US and MRI caused by brain shift, calibration errors, and patient to MRI transformation matrix.

Rigid image registration by General Adaptive Neighborhood matching

This paper aims to propose a new feature and intensity-based image registration method. The proposed approach is based on the block matching algorithm (Ourselin et al., 2000 [1]): a displacement field is locally computed by matching spatially invariant intensity sub-blocks of the images before performing an optimization algorithm from this vector field to estimate the transformation. Our approach proposes a new way to calculate the displacement field by matching spatially variant sub-blocks of the images, called General Adaptive Neighborhoods (GANs) (Debayle and Pinoli, 2006 [2]). These neighborhoods are adaptive with respect to both the intensities and the spatial structures of the image. They represent the patterns within the grayscale images. This paper also presents a consistent shape metric used to match the GANs. The performed qualitative and quantitative experiments show that the proposed GAN matching method provides accurate displacement fields enabling us to perform image rigid registration, even for data from different modalities, that outperforms the classical block matching algorithm with respect to robustness and accuracy criteria.

Entropy-based image registration method using the curvelet transform

Registration is a prerequisite for fusion of geometrically distorted images. Traditionally, intensity-based image registration methods are preferred to feature-based ones due to higher accuracy of the former than that of the latter. To reduce computational load, image registration is often carried out using the approximate-level coefficients of a wavelet-like transform. Directional selectivity of the transform and the objective function used for the coefficients play vital roles in the alignment process of images. This paper introduces an image registration algorithm that uses the approximate-level coefficients of the curvelet transform, directional selectivity of which is better than many wavelet-like transforms. A conditional entropy-based objective function is developed for registration using a suitable probabilistic model of the curvelet coefficients of images. Suitability of the probability distribution of the coefficients is validated using a standard method to assess goodness of fit. To align the distorted images, the affine transformation that possesses parameters related to the translation, rotation, scaling, and shearing is used. Extensive experimentations are carried out to test the performance of the proposed registration method considering that the images are synthetically or naturally distorted. Experimental results show that performance of the proposed registration method is superior to existing methods in terms of commonly used performance metrics.

Revisiting Intensity-Based Image Registration Applied to Mammography

The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases using image registration. This paper presents a quantitative evaluation of state-of-the art intensity based image registration methods applied to mammographic images. These methods range from a global and rigid transformation to local deformable paradigms using various metrics and multiresolution approaches. The aim of this study is to assess the suitability of these methods for mammographic image analysis. Evaluation using temporal cases based on quantitative analysis and a multiobserver study is presented which gives an indication of the accuracy and robustness of the different algorithms. Although previous studies suggested that local deformable methods were not suitable due to the generation of unrealistic distortions, in this work we show that local deformable paradigms (multiresolution B-Spline deformations) obtain the most accurate registration results.

Intensity-Based Image Registration by Nonparametric Local Smoothing

Image registration is used widely in applications for mapping one image to another. Existing image registration methods are either feature-based or intensity-based. Feature-based methods first extract relevant image features and then find the geometrical transformation that best matches the two corresponding sets of features extracted from the two images. Because identification and extraction of image features is often a challenging and time-consuming process, intensity-based image registration, by which the mapping transformation is estimated directly from the observed image intensities of the two images, has received much attention recently. In the literature, most existing intensity-based image registration methods estimate the mapping transformation globally by solving a minimization/maximization problem defined by the two entire images to register. To this end, it needs to be assumed that the mapping transformation has a certain type of parametric form or it is a continuous bivariate function satisfying certain regularity conditions. In this paper, we propose a novel intensity-based image registration method using nonparametric local smoothing. By this method, the mapping transformation at a given pixel is estimated locally in a neighborhood after certain image features are accommodated in the estimation. Due to the flexibility of local smoothing, this method does not require any parametric form for the mapping transformation. It even allows the transformation to be a discontinuous function. Numerical examples show that it is effective in various applications.