An efficient motion estimator with application to medical image registration.

Department of Computer & Information Sciences & Engineering, University of Florida, Gainesville 32611, USA.
Medical Image Analysis (Impact Factor: 3.68). 04/1998; 2(1):79-98. DOI: 10.1016/S1361-8415(01)80029-3
Source: PubMed

ABSTRACT Image registration is a very important problem in computer vision and medical image processing. Numerous algorithms for registering single and multi-modal image data have been reported in these areas. Robustness as well as computational efficiency are prime factors of importance in image data registration. In this paper, a robust/reliable and efficient algorithm for estimating the transformation between two image data sets of a patient taken from the same modality over time is presented. Estimating the registration between two image data sets is formulated as a motion-estimation problem. We use a hierarchical optical flow motion model which allows for both global as well as local motion between the data sets. In this hierarchical motion model, we represent the flow field with a B-spline basis which implicitly incorporates smoothness constraints on the field. In computing the motion, we minimize the expectation of the squared differences energy function numerically via a modified Newton iteration scheme. The main idea in the modified Newton method is that we precompute the Hessian of the energy function at the optimum without explicitly knowing the optimum. This idea is used for both global and local motion estimation in the hierarchical motion model. We present examples of motion estimation on synthetic and real data (from a patient acquired during pre- and post-operative stages) and compare the performance of our algorithm with that of competing ones.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of image registration is to spatially align two or more single-modality images taken at different times, or several images acquired by multiple imaging modalities. Intensity-based registration usually requires optimization of the similarity metric between the images. However, global optimization techniques are too time-consuming, and local optimization techniques frequently fail to search the global transformation space because of the large initial misalignment of the two images. Moreover, for large non-overlapping area registration, the similarity metric cannot reach its optimum value when the two images are properly registered. In order to solve these problems, we propose a novel Symmetric Scale Invariant Feature Transform (symmetric-SIFT) descriptor and develop a fast multi-modal image registration technique. The proposed technique automatically generates a lot of highly distinctive symmetric-SIFT descriptors for two images, and the registration is performed by matching the corresponding descriptors over two images. These descriptors are invariant to image scale and rotation, and are partially invariant to affine transformation. Moreover, these descriptors are symmetric to contrast, which makes it suitable for multi-modal image registration. The proposed technique abandons the optimization and similarity metric strategy. It works with near real-time performance, and can deal with the large non-overlapping and large initial misalignment situations. Test cases involving scale change, large non-overlapping, and large initial misalignment on computed tomography (CT) and magnetic resonance (MR) datasets show that it needs much less runtime and achieves better accuracy when compared to other algorithms.
    Progress in Natural Science 05/2009; 19(5):643–651. · 0.99 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present the GA–SSD–ARC–NLM, a new robust parametric image registration technique based on the non–parametric image registration SSD–ARC algorithm. This new algorithm minimizes a new cost function quite different to the original non-parametric SSD-ARC, which explicitly models outlier punishments, using a combination of a genetic algorithm and the Newton–Levenberg–Marquardt method. The performance of the new method was compared against two robust registration techniques: the Lorentzian Estimator and the RANSAC method. Experimental tests using gray level images with outliers (noise) were done using the three algorithms. The goal was to find an affine transformation to match two images; the new method improves the other methods when noisy images are used.
    Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications; 11/2006
  • Source

Full-text (2 Sources)

Available from
Oct 7, 2014