Dissimilarity Measures for Visual Pattern Partitioning

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/11492542_36We define a visual pattern as an image feature with frequency components in a range of bands that are aligned in phase. A technique to partition an image into its visual patterns involves clustering of the band-pass filtered versions of the image according to a measure of congruence in phase or, equivalently, alignment in the filter’s responses energy maxima. In this paper we study some measures of dissimilarity between images and discuss their suitability to the specific task of misalignment estimation between energy maps.The authors desire to acknowledge the Xunta de Galicia for their financial support of this work by means of the research project PGIDIT04TIC206005PR

Digitisation and 3D reconstruction of 30 year old microscopic sections of human embryo, foetus and orbit

A collection of 2200 microscopic sections was recently recovered at the Netherlands Ophthalmic Research Institute and the Department of Anatomy and Embryology of the Academic Medical Centre in Amsterdam. The sections were created thirty years ago and constitute the largest and most detailed study of human orbital anatomy to date. In order to preserve the collection, it was digitised. This paper documents a practical approach to the automatic reconstruction of a 3- D representation of the original objects from the digitised sections. To illustrate the results of our approach, we show a multi-planar reconstruction and a 3-D direct volume rendering of a reconstructed foetal head

Multimodal Elastic Matching of Brain Images

This paper presents an original method for three-dimensional elastic registration of multimodal images. We propose to make use of a scheme that iterates between correcting for intensity di#erences between images and performing standard monomodal registration. The core of our contribution resides in providing a method that finds the transformation that maps the intensities of one image to those of another. It makes the assumption that there are at most two functional dependences between the intensities of structures present in the images to register, and relies on robust estimation techniques to evaluate these functions. We provide results showing successful registration between several imaging modalities involving segmentations, T1 magnetic resonance (MR), T2 MR, proton density (PD) MR and computed tomography (CT)

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems

Elastic brain image registration using mutual information

Image Registration is the determination of a geometrical transformation that aligns points in one image of an object with corresponding points in another image. The source image is geometrically transformed to match the target image. The geometric transformation can be rigid or non-rigid. Rigid transformations preserve straight lines and angles between straight lines. The basic rigid transformations are rotation, scaling and translation. In this thesis non-rigid registration using B-splines is the method being used to take into account the elastic change in the brain structure. The B-spline equation is a type of curved transformation that does not preserve the straightness of lines, as is the case with rigid transformation. A similarity measure is based on similar pixel values in the image pairs. It is used as a cost function to measure the similarity between the source and target image. Mutual information is a similarity measure based on the probability density function. Optimization of both rigid and non-rigid registration techniques is performed to obtain the registration parameters that define the best geometrical transformation. The parameters are optimized based on the mutual information. Neurosurgery is an application of image registration and requires accurate surgical planning and guidance because of complex and delicate structures in the brain. Over the course of the surgery, the brain changes its shape in reaction to mechanical and physiological changes associated with the surgery such as loss of cerebrospinal fluid and gravity forces

Piecewise Affine Registration of Biological Images for Volume Reconstruction

This manuscript tackles the reconstruction of 3D volumes via mono-modal registration of series of 2D biological images (histological sections, autoradiographs, cryosections, etc.). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. We use as a similarity measure an extension of the classical correlation coefficient that improves the consistency of the field. A hierarchical clustering algorithm then automatically partitions the field into a number of classes from which we extract independent pairs of sub-images. Our clustering algorithm relies on the Earth mover’s distribution metric and is additionally guided by robust least-square estimation of the transformations associated with each cluster. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach on several batches of histological data and discuss its sensitivity to parameters and noise

Automated Image Registration And Mosaicking For Multi-Sensor Images Acquired By A Miniature Unmanned Aerial Vehicle Platform

Algorithms for automatic image registration and mosaicking are developed for a miniature Unmanned Aerial Vehicle (MINI-UAV) platform, assembled by Air-O-Space International (AOSI) L.L.C.. Three cameras onboard this MINI-UAV platform acquire images in a single frame simultaneously at green (550nm), red (650 nm), and near infrared (820nm) wavelengths, but with shifting and rotational misalignment. The area-based method is employed in the developed algorithms for control point detection, which is applicable when no prominent feature details are present in image scenes. Because the three images to be registered have different spectral characteristics, region of interest determination and control point selection are the two key steps that ensure the quality of control points. Affine transformation is adopted for spatial transformation, followed by bilinear interpolation for image resampling. Mosaicking is conducted between adjacent frames after three-band co-registration. Pre-introducing the rotation makes the area-based method feasible when the rotational misalignment cannot be ignored. The algorithms are tested on three image sets collected at Stennis Space Center, Greenwood, and Oswalt in Mississippi. Manual evaluation confirms the effectiveness of the developed algorithms. The codes are converted into a software package, which is executable under the Microsoft Windows environment of personal computer platforms without the requirement of MATLAB or other special software support for commercial-off-the-shelf (COTS) product. The near real-time decision-making support is achievable with final data after its installation into the ground control station. The final products are color-infrared (CIR) composite and normalized difference vegetation index (NDVI) images, which are used in agriculture, forestry, and environmental monitoring

Global optimization algorithms for image registration and clustering

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering. First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points at which the function has been evaluated to decompose the domain into a collection of hyper-rectangles. At each step of the algorithm, it chooses a hyper-rectangle according to a certain criterion and the next function evaluation is at the center of the hyper-rectangle. The dissertation includes a proof that the algorithm converges to the global optimum as the number of function evaluations goes to infinity, and also establishes the convergence rate. Standard test functions are used to experimentally evaluate the algorithm. The second part focuses on applying algorithms from the first part to solve some practical problems. Image processing tasks often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. The dissertation describes a global optimization algorithm and applies it to the problem of identifying the best transformation for aligning two images. Global optimization algorithms can also be applied to the data clustering problem. The basic purpose of clustering is to categorize data into different groups by their similarity. The objective cost functions for clustering usually are non-convex. $k$-means is a popular algorithm which can find local optima quickly but may not obtain global optima. The different starting points for $k$-means can output different local optima. This dissertation describes a global optimization algorithm for approximating the global minimum of the clustering problem. The third part of the dissertation presents variations of the proposed algorithm that work with different assumptions on the available information, including a version that uses derivatives