Image registration by "super-curves"

Decoupled learning for brain image registration

Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.

Plant Species Identification from Occluded Leaf Images

We present an approach to identify the plant species from the contour information from occluded leaf image using a database of full plant leaves. Although contour based 2D shape matching has been studied extensively in the last couple of decades, matching occluded leaves with full leaf databases is an open and little worked on problem. Classifying occluded plant leaves is even more challenging than full leaf matching because of large variations and complexity of leaf structures. Matching an occluded contour with all the full contours in a database is an NP-hard problem, so our algorithm is necessarily suboptimal. First, we represent the 2D contour points as a β-Spline curve. Then, we extract interest points on these curves via the Discrete Contour Evolution (DCE) algorithm. We use subgraph matching using the DCE points as graph nodes, which produces a number of open curves for each closed leaf contour. Next, we compute the similarity transformation parameters (translation, rotation, and uniform scaling) for each open curve. We then "overlay" each open curve with the inverse similarity transformed occluded curve and use the Fréchet distance metric to measure the quality of the match, retaining the best η matched curves. Since the Fréchet metric is cheap to compute but not perfectly correlated with the quality of the match, we formulate an energy functional that is well correlated with the quality of the match, but is considerably more expensive to compute. The functional uses local and global curvature, Shape Context descriptors, and String Cut features. We minimize this energy functional using a convex-concave relaxation framework. The curve among these best η curves, that has the minimum energy, is considered to be the best overall match with the occluded leaf. Experiments on three publicly available leaf image database shows that our method is both effective and efficient, outperforming other current state-of-the-art methods. Occlusion is measured as the percentage of the overall contour (and not leaf area) that is missing. We show that our algorithm can, even for leaves with a high amounts of occlusion (say 50 percent occlusion), still identify the best full leaf match from the databases.

Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields

This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.

A novel contour-based registration of lateral cephalogram and profile photograph

A contour-based automatic registration method of lateral cephalograms and profile photographs is proposed to facilitate the accurate measurement of anteroposterior (AP) position of maxillary central incisors relative to the forehead. There are mainly three steps in the proposed method. First, a hierarchical contour detection algorithm is employed to obtain contours of forehead and nose in lateral cephalograms and profile photographs, respectively. Curve deviation around Nasion of Soft Tissue (Ns) is further corrected using an iterative polynomial curve fitting algorithm according to the characteristics of cephalograms. Second, the coarse registration is performed based on four landmark pairs using a routine least squares method for each image pair, and then the contours are roughly matched. Finally, the coarse registration is further refined by applying the coherent point drift (CPD) algorithm to all the contour points. Using image pairs of lateral cephalograms and profile photographs from 49 Chinese patients, we show that the proposed method achieves higher performance in terms of accuracy and robustness than the existing methods.

Multimodal Image Registration With Line Segments by Selective Search

This paper proposes a line segment-based image registration method. Edges are detected from images by a modified Canny operator, and line segments are then extracted from these edges. At registration, triplets (quaternions) of line segment correspondences are tentatively formed by applying the distance and orientation constraints, which determine an intermediate transformation. Those triplets (quaternions) of lines resulting in higher similarity metrics are preserved, and their intersections are refined by an iterative process or random sample consensus. The proposed method is tested on indoor and outdoor EO/IR image pairs, and the average registration error is calculated to be compared with existing algorithms. Experimental results show that the proposed registration method can robustly align EO/IR images containing line segments, providing more reliable and accurate registration results on multimodal images.

Automatic error correction using adaptive weighting for vessel-based deformable image registration

In this paper, we extend our previous work on deformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embedded tumors, which is common in clinical applications. It is a very challenging task since the registration method that works for homogenous tissues may not work well with inhomogenous tissues. The maximum error normally occurs in the regions with tumors and often exceeds the acceptable error threshold. In this paper, we propose a new error correction method with adaptive weighting to reduce the maximum registration error. Our previous fast deformable registration method is used in the inner loop. We have also proposed a new evaluation metric average error of deformation field (AEDF) to evaluate the registration accuracy in regions between vessels and bifurcation points. We have validated the proposed method using liver MR images from human subjects. AEDF results show that the proposed method can greatly reduce the maximum registration errors when compared with the previous method with no adaptive weighting. The proposed method has the potential to be used in clinical applications to reduce registration errors in regions with tumors.

Matching of large images through coupled decomposition

In this paper, we address the problem of fast and accurate extraction of points that correspond to the same location (named tie-points) from pairs of large-sized images. First, we conduct a theoretical analysis of the performance of the full-image matching approach, demonstrating its limitations when applied to large images. Subsequently, we introduce a novel technique to impose spatial constraints on the matching process without employing subsampled versions of the reference and the target image, which we name coupled image decomposition. This technique splits images into corresponding subimages through a process that is theoretically invariant to geometric transformations, additive noise, and global radiometric differences, as well as being robust to local changes. After presenting it, we demonstrate how coupled image decomposition can be used both for image registration and for automatic estimation of epipolar geometry. Finally, coupled image decomposition is tested on a data set consisting of several planetary images of different size, varying from less than one megapixel to several hundreds of megapixels. The reported experimental results, which includes comparison with full-image matching and state-of-the-art techniques, demonstrate the substantial computational cost reduction that can be achieved when matching large images through coupled decomposition, without at the same time compromising the overall matching accuracy.

Recognition of Center Circles for Encoded Targets in Digital Close-Range Industrial Photogrammetry

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/11.jpg"" width=""300"" /> Final position of center circles</div> Recognition of encoded targets on the industrial backgrounds are hot topics in digital close-rang industrial photogrammetry. To recognize the encoded targets, the center circles of targets need to be detected above all. A method was proposed to spot the center circles accurately based on some criteria put forward by authors and was realized by the program developed in MatlabR2010a. Firstly, according to the image that was preprocessed by image-binary threshold and Canny edge detection, straight lines in image were deleted and dimension criteria was applied to obtain possible contours of center circles. Then all these contours were fitted to ellipses based on the Least Square Algorithm and the candidate ellipses were remained except those that did not meet the shape criteria. Finally because of shooting error, all candidate ellipses were modified to obtain the positions of encoded targets' center circles precisely. In laboratory, an experiment was conducted to test and verify the theory mentioned above and the result showed the robust of the method in this paper. </span>

EDPF: A REAL-TIME PARAMETER-FREE EDGE SEGMENT DETECTOR WITH A FALSE DETECTION CONTROL

We propose a real-time, parameter-free edge/edge segment detection algorithm based on our novel edge/edge segment detector, the edge drawing (ED) algorithm; hence the name edge drawing parameter free (EDPF). EDPF works by running ED with ED's parameters set at their extremes. This produces all edge segments in a given image with numerous false detections. The detected edge segments are then validated by an "a contrario" validation step due to the Helmholtz principle, which eliminates invalid detections leaving only "meaningful" edge segments.

Ray projection for recovering projective transformations and illumination changes

The ray projection and its application to recovering a projective geometric transformation and an affine lighting change between two objects are mathematically studied. A novel technique, viz., variable contour, is proposed for accurately evaluating the ray projection. Moreover, a novel flexible framework of the ray projection is devised for the joint recovery of the eight parameters for the projective transformation and the two parameters for the lighting change. Finally, the framework is experimentally evaluated by recovering a variety of geometric and photometric transformations between real images of indoor and outdoor scenes. Its robustness to image blur and occlusion is demonstrated. Its versatility is illustrated through matching different objects in different classes.

One-dimensional mapping for estimating projective transformations

In this paper, the 1-D mapping is studied for estimating a general projective transformation between images. First, a ray model of image patches is proposed to convert a complex projective deformation of a patch in the Cartesian space into two simple rigid shifts of radial lines in the log-polar/inverse-polar space. To capture these shifts via direct search, two types of radial image line matching, viz. 1-D log-polar mapping and 1-D inverse-polar mapping, are formally defined. Also, the unique features of these shifts are analyzed. Then, a novel two-step mathematical framework is devised for transformation estimation. In the first step, the 1-D log-polar mapping is applied to estimating four affine parameters via two alternative methods for simulated/real images. In the second step, the 1-D inverse-polar mapping is applied to estimating two projective parameters. By a direct search combined with the two-step line matching, two translational parameters are also determined. Finally, the performance of the framework is evaluated first in estimating a variety of projective transformations by comparing the estimates with their ground truths and between the two proposed methods, and second in registering a wide range of challenging image pairs by comparing with a state-of-the-art feature-based method.

Exact feature extraction using finite rate of innovation principles with an application to image super-resolution

The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image super-resolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.