Image Analysis In Materials And Life Sciences
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| Author | : C. Babu Rao |
| Publisher | : |
| Total Pages | : 262 |
| Release | : 2001 |
| Genre | : Science |
| ISBN | : |
The progress of human civilization is punctuated with a number of land mark events in the course of development in science and technology. The attendant continued growth of knowledge with penetrating insights reveal the mysteries of nature and exercise profound influence on human thought. One such thought provoking discipline is image analysis, which made penetrating in-roads into the mysteries of materials and life sciences. This book is a compilation of selected, peer reviewed works received for presentation at SCIAMAL-99.
| Author | : Kota Miura |
| Publisher | : Springer Nature |
| Total Pages | : 178 |
| Release | : 2019-10-17 |
| Genre | : Medical |
| ISBN | : 3030223868 |
This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
| Author | : Gubicza, Jen? |
| Publisher | : IGI Global |
| Total Pages | : 359 |
| Release | : 2014-03-31 |
| Genre | : Technology & Engineering |
| ISBN | : 1466658533 |
X-ray line profile analysis is an effective and non-destructive method for the characterization of the microstructure in crystalline materials. Supporting research in the area of x-ray line profile analysis is necessary in promoting further developments in this field. X-Ray Line Profile Analysis in Materials Science aims to synthesize the existing knowledge of the theory, methodology, and applications of x-ray line profile analysis in real-world settings. This publication presents both the theoretical background and practical implementation of x-ray line profile analysis and serves as a reference source for engineers in various disciplines as well as scholars and upper-level students.
| Author | : Emanuele Trucco |
| Publisher | : Academic Press |
| Total Pages | : 504 |
| Release | : 2019-11-20 |
| Genre | : Computers |
| ISBN | : 0081028164 |
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.
| Author | : Mei Chen |
| Publisher | : Academic Press |
| Total Pages | : 230 |
| Release | : 2020-12-01 |
| Genre | : Computers |
| ISBN | : 0128149736 |
Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation
| Author | : Jens Rittscher |
| Publisher | : Artech House |
| Total Pages | : 533 |
| Release | : 2008 |
| Genre | : Medical |
| ISBN | : 1596932376 |
Here's a first-of-its-kind book that bridges the gap between biomedical imaging and the bioscience community. This unique resource gives you a detailed understanding of imaging platforms, fluorescence imaging, and fundamental image processing algorithms. Further, it guides you through application of advanced image analysis methods and techniques to specific biological problems. The book presents applications that span a wide range of scales, from the detection of signaling events in sub-cellular structures, to the automated analysis of tissue structures. Other critical areas discussed include the dynamics of cell populations and in vivo microscopy. This cutting-edge volume is supported with over 160 illustrations that support key topics throughout the book. CD-ROM Included! Contains full-color images and videos that further illustrate topics discussed in the book.
| Author | : Massimo Fornasier |
| Publisher | : Walter de Gruyter |
| Total Pages | : 351 |
| Release | : 2010-07-30 |
| Genre | : Mathematics |
| ISBN | : 3110226154 |
The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problems, numerical analysis and simulation. The book consists of four lecture notes of courses given at the Summer School on "Theoretical Foundations and Numerical Methods for Sparse Recovery" held at the Johann Radon Institute for Computational and Applied Mathematics in Linz, Austria, in September 2009. This unique collection will be of value for a broad community and may serve as a textbook for graduate courses. From the contents: "Compressive Sensing and Structured Random Matrices" by Holger Rauhut "Numerical Methods for Sparse Recovery" by Massimo Fornasier "Sparse Recovery in Inverse Problems" by Ronny Ramlau and Gerd Teschke "An Introduction to Total Variation for Image Analysis" by Antonin Chambolle, Vicent Caselles, Daniel Cremers, Matteo Novaga and Thomas Pock
| Author | : S. Kevin Zhou |
| Publisher | : Academic Press |
| Total Pages | : 544 |
| Release | : 2023-11-23 |
| Genre | : Computers |
| ISBN | : 0323858880 |
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
| Author | : Ann Wheeler |
| Publisher | : John Wiley & Sons |
| Total Pages | : 285 |
| Release | : 2017-10-12 |
| Genre | : Science |
| ISBN | : 1119096936 |
A comprehensive guide to the art and science of bioimaging data acquisition, processing and analysis Standard and Super-Resolution Bioimaging Data Analysis gets newcomers to bioimage data analysis quickly up to speed on the mathematics, statistics, computing hardware and acquisition technologies required to correctly process and document data. The past quarter century has seen remarkable progress in the field of light microscopy for biomedical science, with new imaging technologies coming on the market at an almost annual basis. Most of the data generated by these systems is image-based, and there is a significant increase in the content and throughput of these imaging systems. This, in turn, has resulted in a shift in the literature on biomedical research from descriptive to highly-quantitative. Standard and Super-Resolution Bioimaging Data Analysis satisfies the demand among students and research scientists for introductory guides to the tools for parsing and processing image data. Extremely well illustrated and including numerous examples, it clearly and accessibly explains what image data is and how to process and document it, as well as the current resources and standards in the field. A comprehensive guide to the tools for parsing and processing image data and the resources and industry standards for the biological and biomedical sciences Takes a practical approach to image analysis to assist scientists in ensuring scientific data are robust and reliable Covers fundamental principles in such a way as to give beginners a sound scientific base upon which to build Ideally suited for advanced students having only limited knowledge of the mathematics, statistics and computing required for image data analysis An entry-level text written for students and practitioners in the bioscience community, Standard and Super-Resolution Bioimaging Data Analysis de-mythologises the vast array of image analysis modalities which have come online over the past decade while schooling beginners in bioimaging principles, mathematics, technologies and standards.
| Author | : Kamalanand, K. |
| Publisher | : IGI Global |
| Total Pages | : 358 |
| Release | : 2018-10-30 |
| Genre | : Medical |
| ISBN | : 1522562443 |
With the technology innovations dentistry has witnessed in all its branches over the past three decades, the need for more precise diagnostic tools and advanced imaging methods has become mandatory across the industry. Recent advancements to imaging systems are playing an important role in efficient diagnoses, treatments, and surgeries. Computational Techniques for Dental Image Analysis provides innovative insights into computerized methods for automated analysis. The research presented within this publication explores pattern recognition, oral pathologies, and diagnostic processing. It is designed for dentists, professionals, medical educators, medical imaging technicians, researchers, oral surgeons, and students, and covers topics centered on easier assessment of complex cranio-facial tissues and the accurate diagnosis of various lesions at early stages.
