Stochastic Distance Transform: Theory, Algorithms and Applications

For numerous applications in image registration, sub-pixel translation estimation is a fundamental task, and increasing attention has been given to methods based on image phase information. However, we have found that none of these methods is universal. In other words, for any one of these methods, we can always find some image pairs which will not be well matched. In this paper, by introducing the cyclic shift matrix (CSM), we present a new model for the translation matching problem and derive a least squares solution for the model. In addition, by repeatedly applying the CSM to the matching image, an iterative CSM method is proposed to further improve the matching accuracy. Furthermore, we show that the traditional phase-based matching algorithms can only achieve an exact solution when there is a cyclic shift relationship between the images to be matched. The proposed method is evaluated using simulated and real images and demonstrates a better performance in both accuracy and robustness compared with the state-of-the-art methods.

The authors apply two approaches to reduce the computation time of the residual complexity similarity metric employed in image registration applications aimed at hardware-based implementations with low-complexity transforms. First, the similarity metric is computed in image sub-blocks, which are subsequently combined into a global metric value. Second, the discrete cosine transform (DCT) needed in the computation of the similarity measure is replaced with multiplier-free low-complexity approximate transforms. The authors propose a new low-complexity transform requiring only 18 additions in an 8 × 8 block and compare it to: the round DCT, the signed DCT, the Hadamard transform and the Walsh-Hadamard transform. Detailed computational complexity analysis reveals that block-wise processing alone reduces computational cost by a factor of 8-9 for original DCT composed of multiplications and additions, and up to ≃4.90 when the proposed DCT is utilised; being the computation performed with additions only. Results obtained from computer simulated and realistic X-ray images demonstrate block-wise processing and approximate transforms result in successful image registration, making residual complexity similarity measure available to hardware-accelerated fast image registration applications.

303. Image registration – precise quality assessment of radiotherapy without necessity of showing corresponding points in simulation and portal images

In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, IR's applications cover a broad range of real-world problems including remote sensing, medical imaging, artificial vision, and computer-aided design. In particular, medical IR is a mature research field with theoretical support and two decades of practical experience. Traditionally, medical IR has been tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of medical IR methods based on the use of metaheuristics such as evolutionary algorithms have been proposed providing outstanding results. The success of the latter modern search methods is related to their ability to perform an effective and efficient global search in complex solution spaces like those tackled in the IR discipline. In this contribution, we aim to develop an experimental survey of the most recognized feature-based medical IR methods considering evolutionary algorithms and other metaheuristics. To do so, the generic IR framework is first presented by providing a deep description of the involved components. Then, a large number of the latter proposals are reviewed. Finally, the most representative methods are benchmarked on two real-world medical scenarios considering two data sets of three-dimensional images with different modalities.

Continuum mechanical models have been used to regularize ill-posed problems in many applications in medical imaging analysis such as image registration and left ventricular motion estimation. In this work, we present a significant extension to the common elastic model which we call the active elastic model. The active elastic model is designed to reduce bias in deformation estimation and to allow the imposition of proper priors on deformation estimation problems that contain information regarding both the expected magnitude and the expected variability of the deformation to be estimated. We test this model on the problem of left ventricular deformation estimation, and present ideas for its application in image registration and brain deformation during neurosurgery.KeywordsImage RegistrationElastic ModelActive Shape ModelMedical Image AnalysisPassive ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Similarity transformation parameters recovery based on Radon transform. Application in image registration and object recognition

The presentation gives and overviews of two super-resolution fluorescence microscopy techniques are presented which are inspired by single-molecule spectroscopy. The first is Superresolution Optical Fluctuation Imaging (SOFI), which converts temporal intensity fluctuations of emitters into an enhanced spatial resolution of an image. Besides providing higher resolution, SOFI is also very efficient in suppressing any background or auto-fluorescence, increases image contrast, and allows for recording three-dimensional images (z-sectioning) with a wide field microscope. The fundamental principles behind SOFI as well as several applications for super-resolved, three-dimensional cell imaging will be presented.The second technique is Metal-Induced Energy-Transfer Imaging. When placing a fluorescent molecule close to a metal, its fluorescence properties change dramatically. In particular, one observes a strongly modified lifetime of its excited state (Purcell effect). We call this effect metal-induced energy transfer or MIET. The MIET-coupling between an excited emitter and a metal film is strongly dependent on the emitter's distance from the metal. We have used this effect for mapping the basal membrane of live cells with an axial accuracy of ∼3 nm. The method is easy to implement and does not require any change to a conventional fluorescence lifetime microscope; it can be applied to any biological system of interest, and is compatible with most other super-resolution microscopy techniques which enhance the lateral resolution of imaging. Moreover, it is even applicable to localizing individual molecules, thus offering the prospect of using single-molecule localization microscopy for structural studies of biomolecules and biomolecular complexes.

It has been recently shown that morphological openings and closings can be viewed as consistent MAP es- timators of morphologically smooth signals in i.i.d. noise. We revisit this viewpoint under a different set of assumptions, which allows the explicit incorporation of geometric and morphological constraints into the noisemodel, i.e., the noise may now exhibit geometric structure; surprisingly, it turns out that this affects neither theoptimality nor the consistency of these filters.Keywords: Opening, Closing, MAP Optimality, Consistency of MAP Estimator 1 INTRODUCTION In two recent papers24'25 Sidiropoulos et al. have obtained statistical proofs of MAP optimality and strongconsistency of morphological openings and closings. These results were made possible by casting the filtering problem within a general framework of Uniformly Bounded Discrete Random Set (or, Discrete Random Set (DRS) , for short) theory.23'2'•A DRS X is simply defined as a measurable mapping from some probability space to a measurable space(E(B), E(E(B))), where E(B) is a complete lattice with a finite least upper bound (usually, the power set of some

We have developed a new representation of shape in grayscale images that does not rely on the detection of edges. Objects are described by computing features that indicate the geometric stucture of multiscale neighborhoods of each pixel. We define a multiscale medial axis (MMA) by considering the maximum resoponse over scale to the Laplacian operator at each pixel. The MMA is a connected ridgetop stucture. Associated with each pixel on the Mh4A is a width function determined by the scale at which the maximum response to the Laplacian occurs. This width function gives the approximate location of the object’s boundaries. The outputs of multiscale orientation-specific filters give estimates of axis orientation and boundary curvature. This image description has been shown to produce sensible regions for use in image segmentation and image registration applications.

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

Objective To investigate the application of CT image and cone beam computed tomography (CBCT) image registration based on 3D Slicer software in image-guided radiotherapy for uterine cervical neoplasms. Methods Based on 3D Slicer software and Slicer RT toolkit, 10 positioning CT images and 50 CBCT images of 10 patients with uterine cervical neoplasms in Henan Provincial People's Hospital between January 2018 and October 2018 had rigid registration and b-spline deformation registration respectively. The dice similarity coefficient (DSC) and Hausdorff distance (HD) of the bladder, rectum, femoral head, spinal cord, and body of CT-CBCT images were compared by using paired t-test before and after the registration. Results Pre-registration, rigid registration and after b-spline deformation registration of CT images and CBCT images, the DSC in the bladder (0.459±0.177, 0.528±0.184, 0.542±0.187, respectively), the rectum (0.564±0.141, 0.632±0.091, 0.684±0.097, respectively), the femoral head (0.695±0.088, 0.833±0.030, 0.865±0.027, respectively), the spinal cord (0.587±0.119, 0.746±0.085, 0.834±0.032, respectively) and the body surface (0.922±0.013, 0.948±0.011, 0.959±0.009, respectively) showed an increased trend; HD in the bladder (12.8±7.2, 12.2±7.1, 11.7±7.3, respectively), the rectum (5.0±1.8, 4.4±1.2, 3.4±1.2, respectively), the femoral head (3.6±1.2, 1.8±0.5, 1.5±0.5, respectively), the spinal cord (4.0±1.0, 2.7±1.3, 1.8±0.5, respectively) and the body surface (6.3±2.1, 5.2±2.0, 4.3±2.0, respectively) showed a decreased trend. The differences of pairwise comparison in the same parts were statistically significant (all P < 0.05). Conclusions Both rigid registration and b-spline deformation registration of CT-CBCT images based on 3D Slicer software can improve the radiotherapy accuracy of uterine cervical neoplasms, and b-spline deformation registration has more significant advantages. Key words: Uterine cervical neoplasms; Radiotherapy; 3D Slicer software; Cone-beam computed tomography; Image registration; Image-guided radiation therapy

In image registration of medical data a common and challenging problem is handling intensity-inhomogeneities. These inhomogeneities appear for instance in images of serially sectioned brains caused by the histological staining process or in medical imaging with contrast agents. Beneath this, natural outliers (for instance cells or vessels) produced by the underlying material itself may be mistaken as noise. Both image registration applications have in common that the well known sum of squared differences (SSD) measure would detect false differences. To deal with these kinds of problems, we supplement the common SSD-measure with image derivatives of higher order. Additionally we introduce a non-quadratic penalizer function to the distance measure leading to robust energy. The concepts are well known in optical flow. Overall, we present a variational model which combines all of these properties. This formulation leads to a fast and efficient algorithm. We demonstrate its applicability at the problems described above.

ABSTRACTMycoplasmas are the smallest free-living organisms. These bacteria are important models for both fundamental and synthetic biology, owing to their highly reduced genomes. They are also relevant in the medical and veterinary fields, as they are pathogenic to both humans and most livestock species. Mycoplasma cells have minute sizes, often in the 300- to 800-nm range. As these dimensions are close to the diffraction limit of visible light, fluorescence imaging in mycoplasmas is often poorly informative. Recently developed superresolution imaging techniques can break this diffraction limit, improving the imaging resolution by an order of magnitude and offering a new nanoscale vision of the organization of these bacteria. These techniques have, however, not been applied to mycoplasmas before. Here, we describe an efficient and reliable protocol to perform single-molecule localization microscopy (SMLM) imaging in mycoplasmas. We provide a polyvalent transposon-based system to express the photoconvertible fluorescent protein mEos3.2, enabling photo-activated localization microscopy (PALM) in most Mycoplasma species. We also describe the application of direct stochastic optical reconstruction microscopy (dSTORM). We showcase the potential of these techniques by studying the subcellular localization of two proteins of interest. Our work highlights the benefits of state-of-the-art microscopy techniques for mycoplasmology and provides an incentive to further the development of SMLM strategies to study these organisms in the future.IMPORTANCE Mycoplasmas are important models in biology, as well as highly problematic pathogens in the medical and veterinary fields. The very small sizes of these bacteria, well below a micron, limits the usefulness of traditional fluorescence imaging methods, as their resolution limit is similar to the dimensions of the cells. Here, to bypass this issue, we established a set of state-of-the-art superresolution microscopy techniques in a wide range of Mycoplasma species. We describe two strategies: PALM, based on the expression of a specific photoconvertible fluorescent protein, and dSTORM, based on fluorophore-coupled antibody labeling. With these methods, we successfully performed single-molecule imaging of proteins of interest at the surface of the cells and in the cytoplasm, at lateral resolutions well below 50 nm. Our work paves the way toward a better understanding of mycoplasma biology through imaging of subcellular structures at the nanometer scale.

In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.

In this paper, we describe improvements to the particle swarm optimizer (PSO) made by the inclusion of an unscented Kalman filter to guide particle motion. We show how this method increases the speed of convergence, and reduces the likelihood of premature convergence, increasing the overall accuracy of optimization. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve the performance. The PSOs were tested firstly on a number of common synthetic benchmarking functions and secondly applied to a practical three-dimensional image registration problem. The proposed methods displayed better performances for 4 out of 8 benchmark functions and reduced the target registration errors by at least 2mm when registering down-sampled benchmark brain images. They also demonstrated an ability to align images featuring motion-related artifacts which all other methods failed to register. These new PSO methods provide a novel, efficient mechanism to integrate prior knowledge into each iteration of the optimization process, which can enhance the accuracy and speed of convergence in the application of medical image registration.