Methods for Reliable Image Registration : Algorithms, Distance Measures, and Representations

Much biomedical and medical research relies on the collection of ever-larger amounts of image data (both 2D images and 3D volumes, as well as time-series) and increasingly from multiple sources. Image registration, the process of finding correspondences between images based on the affinity of features of interest, is often required as a vital step towards the final analysis, which may consist of a comparison of images, measurement of movement, or fusion of complementary information. The contributions in this work are centered around reliable image registration methods for both 2D and 3D images with the aim of wide applicability: similarity and distance measures between images for image registration, algorithms for efficient computation of these, and other commonly used measures for both local and global optimization frameworks, and representations for multimodal image registration where the appearance and structures present in the images may vary dramatically. The main contributions are: (i) distance measures for affine symmetric intensity image registration, combining intensity and spatial information based on the notion of alpha-cuts from fuzzy set theory; (ii) the extension of the affine registration method to more flexible deformable transformation models, leading to the framework Intensity and Spatial Information-Based Deformable Image Registration (INSPIRE); (iii) two efficient algorithms for computing the proposed distances and their spatial gradients and thereby enabling local gradient-based optimization; (iv) a contrastive representation learning method, Contrastive Multimodal Image Representation for Registration (CoMIR), utilizing deep learning techniques to obtain common representations that can be registered using methods designed for monomodal scenarios; (v) efficient algorithms for global optimization of mutual information and similarities of normalized gradient fields; (vi) a comparative study exploring the applicability of modern image-to-image translation methods to facilitate multimodal registration; (vii) the Stochastic Distance Transform, using the theory of discrete random sets to offer improved noise-insensitivity to distance computations; (viii) extensive evaluation of the proposed image registration methods on a number of different datasets mainly from (bio)medical imaging, where they exhibit excellent performance, and reliability, suggesting wide utility

Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment

Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. The information-theoretic concept of mutual information (MI) is widely used as a similarity measure to guide multimodal alignment processes, where most works have focused on local maximization of MI that typically works well only for small displacements; this points to a need for global maximization of MI, which has previously been computationally infeasible due to the high run-time complexity of existing algorithms. We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain. We show that the algorithm is equivalent to a direct method while asymptotically superior in terms of run-time. Furthermore, we propose a method for multimodal image alignment for transformation models with few degrees of freedom (e.g. rigid) based on the proposed CMIF-algorithm. We evaluate the efficacy of the proposed method on three distinct benchmark datasets, of aerial images, cytological images, and histological images, and we observe excellent success-rates (in recovering known rigid transformations), overall outperforming alternative methods, including local optimization of MI as well as several recent deep learning-based approaches. We also evaluate the run-times of a GPU implementation of the proposed algorithm and observe speed-ups from 100 to more than 10,000 times for realistic image sizes compared to a GPU implementation of a direct method. Code is shared as open-source at \url{github.com/MIDA-group/globalign}.Comment: 7 pages, 4 figures, 2 tables. The article is under consideration at Pattern Recognition Letter

Why go headless – a comperative study between traditional CMS and the emerging headless trend

There has been an exponential increase in the number of websites, digital channels and consequently digital content in the last years. Not only are the number of websites increasing but they are also becoming more complex, therefore it is no longer feasible to handle content and code with the same tools. Content Management Systems (CMS) are the solution to this problem and offers a way of managing content. The market today offers a broad variety of solutions that each have their own advantages, one of the more common being WYSWYG-functionality which often means that the functionality and the presentation of the content are tightly coupled. "Headless" CMS are a new way of doing things and offers the user a way of managing content without presenting them with a way of displaying the content. The different types of CMS present advantages and disadvantages from a user centred point of view as well as from a technical one. The thesis aims to explore these perspectives and form a hypothesis based on the studied cases. The study presents a set of aspects that based on the context in which the CMS is used and implemented can be perceived as either advantages or disadvantages. "Headless" CMS however shows a tendency to be the preferable choice where the editors have a technical background and the developing part values an agnostic approach when implementing a CMS, whereas a traditional CMS with WYSIWYG functionality tends to be more favourable where stability and editorial freedom are valued

Similarity of Hybrid Object Representations With Applications in Object Recognition and Classification

Similarity measures between images that are robust to noise and other kinds ofdistortion, while sensitive to transformations in a smooth and stable way, are of great importance in many image analysis problems. In this thesis a family of measures basedon fuzzy set theory which combine shape and intensity, is extended to vector-valued fuzzy sets for hybrid object representations such as intensity and gradient magnitudeas well as multi-spectral images such as color images. Several novel distance measures are proposed, discussed with regards to theoretical and practical properties, and evaluated empirically on both synthetic images and real-life object recognition and classification tasks. Performance metrics, such as number of local minima and size of catchment basin, which are important for distance-based local search techniques are evaluated for varying degrees of distortion by additive noise and number of discrete membership levels. The proposed distance measures are shown to enable utilizationof information-rich object representations and to outperform distance measures between scalar-valued fuzzy sets on various object detection and classification tasks