We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF distance measure is minimized with respect to an increasingly flexible transformation. We apply the method in the ANHIR image registration challenge and evaluate its performance on the training data. The presented method is robust (error reduction in 99.6 of the cases), fast (runtime < 4 seconds) and accurate (median relative target registration error 0.0019).
Lung registration in thoracic CT scans has received much attention in the medical imaging community. Possible applications range from follow-up analysis, motion correction for radiation therapy, monitoring of air flow and pulmonary function to lung elasticity analysis. In a clinical environment, runtime is always a critical issue, ruling out quite a few excellent registration approaches. In this paper, a highly efficient variational lung registration method based on minimizing the normalized gradient fields distance measure with curvature regularization is presented. The method ensures diffeomorphic deformations by an additional volume regularization. Supplemental user knowledge, like a segmentation of the lungs, may be incorporated as well. The accuracy of our method was evaluated on 40 test cases from clinical routine. In the EMPIRE10 lung registration challenge, our scheme ranks third, with respect to various validation criteria, out of 28 algorithms with an average landmark distance of 0.72 mm. The average runtime is about 1:50 min on a standard PC, making it by far the fastest approach of the top-ranking algorithms. Additionally, the ten publicly available DIR-Lab inhale-exhale scan pairs were registered to subvoxel accuracy at computation times of only 20 seconds. Our method thus combines very attractive runtimes with state-of-the-art accuracy in a unique way. Image registration is central to many challenges in medical imaging and therefore it has a vast range of applications. The purpose of this note is to provide a unified but extremely flexible framework for image registration. This framework is based on a variational formulation of the registration problem. We discuss the framework as well as some of its most important building blocks. These include some of the most promising non-linear registration strategies used in today medical imaging. The overall goal of image registration is to compute a deformation, such that a deformed version of an image becomes similar to a so-called reference image. Hence, the similarity measure is an important building block. Depending on the application at hand, it is inevitable to constrain the wanted deformation in an appropriate way. Thus, regularization is also a main building block. Finally, it is often desirable to incorporate higher level information about the expected deformation. We show how such constraints or information can easily be integrated in our general framework and discuss some examples. Moreover, the proposed general framework allows for a unified algorithmic treatment of the various building blocks. Whenever images taken at different times, from different viewpoints, and or by different sensors need to be compared, merged, or integrated, image registration is required. Registration, also known as alignment, fusion, or warping, is the process of transforming data into a common reference frame. This book provides an overview of state-of-the-art registration techniques from theory to practice, plus numerous exercises designed to enhance readers understanding of the principles and mechanisms of the described techniques. It also provides, via a supplementary Web page, free access to FAIR.m, a package that is based on the MATLAB software environment, which enables readers to experiment with the proposed algorithms and explore the presented examples in more depth. Written from an interdisciplinary point of view, this book will appeal to mathematicians who want to learn more about image registration, medical imaging professionals who want to know more about and explore available imaging techniques, and computer scientists and engineers who want to understand the numerical schemes behind the techniques. The book is also appropriate for use as a course text at the advanced graduate level. Contents: FAIR Listings; FAIR Examples; List of Figures; List of Tables; Preface; Chapter 1: Introduction; Chapter 2: FAIR Concepts; Chapter 3: Image Interpolation; Chapter 4: Transforming Images by Parameterized Transformations; Chapter 5: Landmark-Based Registration; Chapter 6: Parametric Image Registration; Chapter 7: Distance Measures; Chapter 8: Regularization; Chapter 9: Nonparametric Image Registration; Chapter 10: Outlook; Bibliography; Symbols, Acronyms, Index. We present a novel computational approach to fast and memory-efficient deformable image registration. In the variational registration model, the computation of the objective function derivatives is the computationally most expensive operation, both in terms of runtime and memory requirements. In order to target this bottleneck, we analyze the matrix structure of gradient and Hessian computations for the case of the normalized gradient fields distance measure and curvature regularization. Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic. This has further benefits: (1) matrix computations can be fully parallelized, (2) memory complexity for derivative computation is reduced from linear to constant, and (3) overall computation times are substantially reduced. In comparison with an optimized matrix-based reference implementation, the CPU implementation ac... The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods. Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 ?m thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often exhibit inhomogenities of the gray level distribution coming from staining and or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground objects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rotation and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point. Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations. In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered rat brain sections and the first one hundred images of serial histologic sections through both occipital lobes of a human brain (6112 images). Additionally, we will describe how a parallel implementation of the elastic registration was realized. Finally, the computed force fields have been applied here for the first time to the morphometrized data of cells determined automatically by an image analytic framework.