Automatic image registration using evolutionary algorithm

Image registration is a process to obtain the spatial transformation of an input image to a reference image by which similarity measure is optimized between the two images. Mutual information is a similarity measure based on information theory used in the process of image registration. Mutual information compares the statistical dependency between images. Registration based on mutual information is robust and could use for a large class of mono modality and multimodality images. In this work we use mutual information as the similarity measure .There is a requirement to finding the global maxima of similarity measure, for this we use two algorithm simple genetic algorithm and share genetic algorithm and compare the result of these algorithm. In these optimization technique require several decision to made during implementation, such as encoding, selection method and evolution operator. In this work we use two selection method roulette-wheel method and tournament selection method. Result indicates that these optimization techniques can be used for efficient image registratio

Image Registration Using Redundant Wavelet Transforms

Imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multiple images. Since human analysis is tedious and error prone for large data sets, we require an automatic, efficient, robust, and accurate method to register images. Wavelet transforms have proven useful for a variety of signal and image processing tasks, including image registration. In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency. The shift-invariant wavelet transform is applied in translation estimation and a new rotation-invariant polar wavelet transform is effectively utilized in rotation estimation. We demonstrate the robustness of these redundant wavelet transforms for the registration of two images (i.e., translating or rotating an input image to a reference image), but extensions to larger data sets are certainly feasible. We compare the registration accuracy of our redundant wavelet transforms to the \u27critically sampled\u27 discrete wavelet transform using the Daubechies (7,9) wavelet to illustrate the power of our algorithm in the presence of significant additive white Gaussian noise and strongly translated or rotated images

An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images

Image registration using finite dimensional lie groups : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mathematics at Massey University, Palmerston North, New Zealand

D'Arcy Thompson was a biologist and mathematician who, in his 1917 book `On Growth and Form', posited a `Theory of Transformations', which is based on the observation that a smooth, global transformation of space may be applied to the shape of an organism so that its transformed shape corresponds closely to that of a related organism. Image registration is the computational task of finding such transformations between pairs of images. In modern applications in areas such as medical imaging, the transformations are often chosen from the infinite-dimensional diffieomorphism group. However, this differs from Thompson's approach where the groups are chosen to be as simple as possible, and are generally finite-dimensional. The main exception to this is the similarity group of translation, rotation, and scaling, which is used to pre-align images. In this thesis the set of planar Lie groups are investigated and applied to image registration of the types of images that Thompson considered. As these groups are smaller, successful registration in these groups provides more specifc information about the relationship between the images than diffeomorphic registration does, as well as providing faster implementations. We build a lattice of the Lie groups showing which are subgroups of each other, and the groups are used to perform image registration by minimizing the L2-norm of the difference between the group-transformed source image and the target image. A robust, practical, and efficient algorithm for registration in Lie groups is developed and tested on a variety of image types. Each successful registration returns a point in a Lie group. Given several related images (such as the hooves of several animals) it is possible to find smooth curves that pass through the Lie group elements used to relate the various images. These curves can then be employed to interpolate points between the set of images or to extrapolate to new images that have not been seen before. We discuss the mathematics behind this and demonstrate it on the images that Thompson used, as well as other datasets of interest. Finally, we consider using a sequence of the planar Lie groups to perform registration, with the output from one group being used as the input to the next. We call this multiregistration, and have identified two types: where the smallest group is a subgroup of the next smallest, and so on up a chain, and where the groups are not directly related, i.e., separated on the lattice. We demonstrate experimentally that multiregistration can provide more information about the relationship between images than simple registration. In addition, we show that transformations that cannot be obtained by a single registration in any of the groups considered can be successfully reached

New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.Comment: 15 pages, 9 figures, accepted to ApJ. Replaced with accepted version; mostly minor changes. Software update

Surgical planning tool for robotically assisted hearing aid implantation

PURPOSE : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. METHODS :    Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. RESULTS :    The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of [Formula: see text] mm. CONCLUSIONS :    Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time

Locally Non-rigid Registration for Mobile HDR Photography

Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image. Unfortunately, existing methods to address this problem are either accurate, but too slow for mobile devices, or fast, but prone to failing. We propose a method that fills this void: our approach is extremely fast---under 700ms on a commercial tablet for a pair of 5MP images---and prevents the artifacts that arise from insufficient registration quality