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As its title suggests, this innovative book has been written for life scientists needing to analyse their data sets, and programmers, wanting a better understanding of the types of experimental images life scientists investigate on a regular basis. Each chapter presents one self-contained biomedical experiment to be analysed. Part I of the book presents its two basic ingredients: essential concepts of image analysis and Matlab. In Part II, algorithms and techniques are shown as series of "recipes" or solved examples that show how specific techniques are applied to a biomedical experiments like Western Blots, Histology, Scratch Wound Assays and Fluoresence. Each recipe begins with simple techniques that gradually advance in complexity. Part III presents some advanced techniques for the generation of publication quality figures. The book does not assume any computational or mathematical expertise. A practical, clearly-written introduction to biomedical image analysis that provides the tools for life scientists and engineers to use when solving problems in their own laboratories. Presents the basic concepts of MATLAB software and uses it throughout to show how it can execute flexible and powerful image analysis programs tailored to the specific needs of the problem. Within the context of four biomedical cases, it shows algorithms and techniques as series of "recipes", or solved examples that show how a particular technique is applied in a specific experiment. Companion website containing example datasets, MATLAB files and figures from the book.
This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.
As its title suggests, this innovative book has been written for life scientists needing to analyse their data sets, and programmers, wanting a better understanding of the types of experimental images life scientists investigate on a regular basis. Each chapter presents one self-contained biomedical experiment to be analysed. Part I of the book presents its two basic ingredients: essential concepts of image analysis and Matlab. In Part II, algorithms and techniques are shown as series of "recipes" or solved examples that show how specific techniques are applied to a biomedical experiments like Western Blots, Histology, Scratch Wound Assays and Fluoresence. Each recipe begins with simple techniques that gradually advance in complexity. Part III presents some advanced techniques for the generation of publication quality figures. The book does not assume any computational or mathematical expertise. A practical, clearly-written introduction to biomedical image analysis that provides the tools for life scientists and engineers to use when solving problems in their own laboratories. Presents the basic concepts of MATLAB software and uses it throughout to show how it can execute flexible and powerful image analysis programs tailored to the specific needs of the problem. Within the context of four biomedical cases, it shows algorithms and techniques as series of "recipes", or solved examples that show how a particular technique is applied in a specific experiment. Companion website containing example datasets, MATLAB files and figures from the book.
The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.
Zu dem Thema gibt es viele Publikationen, die von Experten für Experten geschrieben wurden. Dieses Buch wendet sich insbesondere an Studenten höherer Semester und Forscher, denen das Hintergrundwissen der Physik fehlt, um neuartige Verfahren der Fluoreszenzmikroskopie zu verstehen. Die zweite Auflage wartet mit neuen Kapiteln und einer erweiterten Einführung auf. Der Schwerpunkt liegt auf der hochauflösenden und Einzelmolekül-Mikroskopie. Jedes Kapitel wurde von einem anerkannten Experten des Fachgebiets geschrieben und sorgfältig überarbeitet, um so die Entwicklungen der letzten Jahre wiederzugeben.
In November 2015, Buenos Aires, Argentina became the location of several important events for geo-professionals, with the simultaneous holding of the 6th International Symposium on Deformation Characteristics of Geomaterials, the 15th Pan-American Conference on Soil Mechanics and Geotechnical Engineering (XV PCSMGE), the 8th South American Congress on Rock Mechanics (SCRM), as well as the 22nd Argentinean Congress of Geotechnical Engineering (CAMSIGXXII). This synergy provided a unique opportunity to exchange ideas and discuss current and future practices in the areas of soil mechanics and rock mechanics, and their applications in civil, energy, environmental, and mining engineering. This book presents the proceedings of the 6th International Symposium on Deformation Characteristics of Geomaterials. As well as 118 articles selected for publication after peer review, it includes 7 lectures delivered by invited keynote speakers and the Third Bishop Lecture, delivered by Professor Herve Di Benedetto of the University of Lyon, France, who presented a reference work on the advanced testing and modeling of bituminous bounded and unbounded granular materials. The conference brought together practitioners, researchers and educators from around the world engaged in the understanding of the deformation properties of geo-materials before failure, and the small strain parameters as fundamental characteristics of geo-materials. The main topics covered by the symposium include experimental investigations from very small strains to beyond failure, including multi-physical approach; HTC M coupling behavior, characterization and modeling of various geo-materials and interfaces; and practical prediction and interpretation of ground responses: field observation and case histories.
Imaging mass spectrometry (MS) techniques are often utilized without an understanding of their underlying principles, making it difficult for scientists to determine when and how they can exploit MS to visualize their biomolecules of interest. Introduction to Spatial Mapping of Biomolecules by Imaging Mass Spectrometry is an essential reference to help scientists determine the status and strategies of biomolecule analysis, describing its many applications for diverse classes of biomolecules. The book builds a foundation of imaging MS knowledge by introducing ionization sources, sample preparation, visualization guidelines, molecule identification, quantification, data analysis, etc. The second section contains chapters focused on case studies on analyzing a biomolecule class of molecules. Case studies include an introduction/background, and a summary of successful imaging MS studies with illustrative figures and future directions. - Provides the introductory foundations of imaging mass spectrometry for those new to the technique - Organized by topic to facilitate a quick deep dive, allowing researchers to immediately apply the imaging MS techniques to their work - Includes case studies summarizing the imaging MS techniques developed for the class of molecules
Ideal for classroom use and self-study, this book explains the implementation of the most effective modern methods in image analysis, covering segmentation, registration and visualisation, and focusing on the key theories, algorithms and applications that have emerged from recent progress in computer vision, imaging and computational biomedical science. Structured around five core building blocks - signals, systems, image formation and modality; stochastic models; computational geometry; level set methods; and tools and CAD models - it provides a solid overview of the field. Mathematical and statistical topics are presented in a straightforward manner, enabling the reader to gain a deep understanding of the subject without becoming entangled in mathematical complexities. Theory is connected to practical examples in x-ray, ultrasound, nuclear medicine, MRI and CT imaging, removing the abstract nature of the models and assisting reader understanding.
Digital image processing and analysis is a field that continues to experience rapid growth, with applications in many facets of our lives. Areas such as medicine, agriculture, manufacturing, transportation, communication systems, and space exploration are just a few of the application areas. This book takes an engineering approach to image processing and analysis, including more examples and images throughout the text than the previous edition. It provides more material for illustrating the concepts, along with new PowerPoint slides. The application development has been expanded and updated, and the related chapter provides step-by-step tutorial examples for this type of development. The new edition also includes supplementary exercises, as well as MATLAB-based exercises, to aid both the reader and student in development of their skills.
Short compute times are crucial for timely diagnostics in biomedical applications, but lead to a high demand in computing for new and improved imaging techniques. In this book reconfigurable computing with FPGAs is discussed as an alternative to multi-core processing and graphics card accelerators. Instead of adjusting the application to the hardware, FPGAs allow the hardware to also be adjusted to the problem. Acceleration of Biomedical Image Processing with Dataflow on FPGAs covers the transformation of image processing algorithms towards a system of deep pipelines that can be executed with very high parallelism. The transformation process is discussed from initial design decisions to working implementations. Two example applications from stochastic localization microscopy and electron tomography illustrate the approach further. Topics discussed in the book include:• Reconfigurable hardware• Dataflow computing• Image processing• Application acceleration