Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. Gives state-of-the-art methods in (bio)medical image synthesis Explains the principles (background) of image synthesis methods Presents the main applications of biomedical image synthesis methods
The use of computer-based image analysis systems for all kinds of images, but especially for microscope images, has become increasingly widespread in recent years, as computer power has increased and costs have dropped. Software to perform each of the various tasks described in this book exists now, and without doubt additional algorithms to accomplish these same things more efficiently, and to perform new kinds of image processing, feature discrimination and measurement, will continue to be developed. This is likely to be true particularly in the field of three-dimensional imaging, since new microscopy methods are beginning to be used which can produce such data. It is not the intent of this book to train programmers who will assemble their own computer systems and write their own programs. Most users require only the barest of knowledge about how to use the computer, but the greater their understanding of the various image analysis operations which are possible, their advantages and limitations, the greater the likelihood of success in their application. Likewise, the book assumes little in the way of a mathematical background, but the researcher with a secure knowledge of appropriate statistical tests will find it easier to put some of these methods into real use, and have confidence in the results, than one who has less background and experience. Supplementary texts and courses in statistics, microscopy, and specimen preparation are recommended as necessary.
This book is thesecond volume of a three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment" that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basic deterministic technique is to fit a model trajectory to the observations over a period of time to within the observation error. Since the model is not perfect the model trajectory has to be corrected, which defines the data assimilation problem. The stochastic view can be expressed by using an ensemble of model trajectories, and calculating corrections to both the mean value and the spread which allow the observations to be fitted by each ensemble member. In other areas of earth science, only the structure of the model formulation itself is known and the aim is to use the past observation history to determine the unknown model parameters. The book records the achievements of Workshop2 "Large-Scale Inverse Problems and Applications in the Earth Sciences". Itinvolves experts in the theory of inverse problems together with experts working on both theoretical and practical aspects of the techniques by which large inverse problems arise in the earth sciences.
This volume explores open-source based image analysis techniques to provide a state-of-the-art collection of workflows covering current bioimage analysis problematics, including colocalization, particle counting, 3D structural analysis, ratio imaging and FRET quantification, particle tracking, high-content screening or machine learning. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and scripts, step-by-step, readily reproducible image analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and cutting-edge, Computer Optimized Microscopy: Methods and Protocols is a valuable resource for both novice and experienced researchers who are interested in learning more about this developing field. .
Englische Version: Since the invention of the microscope, microscopy images have generated new insights in biomedical research. While in the past these images were used for illustrative purposes, state-of-the-art microscopy images provide quantitative measurements. Moreover, modern microscopes are capable of autonomously producing large image datasets of increasing complexity, rendering manual analysis inefficient if not infeasible. Thus, extracting biologically relevant information from these datasets requires computational analysis using appropriate algorithms and software. While some analysis methods generalize to different microscope set-ups and types of images, others need to be well tailored to a particular problem. In this work, I present two new methods for automated image analysis of microscopy data. First, Fourier ring correlation-based quality estimation (FRC-QE) is a new metric for automated image quality estimation of 3D fluorescence microscopy acquisitions. I benchmarked the method in the context of evaluating clearing efficiency in human brain organoids. FRC-QE automates image quality control, a task that is often performed manually and thereby represents a bottleneck when scaling image-based experiments to thousand or more images. The method can estimate clearing efficiency across experimental replicates and clearing protocols. It generalizes to different microscopy modalities and efficiently scales to thousands of images. Second, I have developed a new method for behavioral imaging of C. elegans larvae. [...].
Scientists and engineers have long relied on the power of imaging techniques to help see objects invisible to the naked eye, and thus, to advance scientific knowledge. These experts are constantly pushing the limits of technology in pursuit of chemical imagingâ€"the ability to visualize molecular structures and chemical composition in time and space as actual events unfoldâ€"from the smallest dimension of a biological system to the widest expanse of a distant galaxy. Chemical imaging has a variety of applications for almost every facet of our daily lives, ranging from medical diagnosis and treatment to the study and design of material properties in new products. In addition to highlighting advances in chemical imaging that could have the greatest impact on critical problems in science and technology, Visualizing Chemistry reviews the current state of chemical imaging technology, identifies promising future developments and their applications, and suggests a research and educational agenda to enable breakthrough improvements.
Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.
Image Analysis of Food Microstructure offers a condensed guide to the most common procedures and techniques by which quantitative microstructural information about food can be obtained from images. The images are selected from a broad range of food items, including macroscopic images of meat and finished products such as pizza, and the microstructu
