June 18, 2021

Download Ebook Free Computer Vision For Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
Author : Mei Chen, Ph.D
Publisher : Academic Press
Release Date : 2019-02-15
Category : Computers
Total pages :350
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Computer Vision for Microscopy Image Analysis provides a broad and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, showing how they can be applied to biological and medical data. Topics covered include sections on how computer vision analysis can automate and enhance human assessment of microscopy images for discovery, the important steps in microscopy image analysis, state-of-the-art methods for microscopy image analysis, how high-throughput microscopy enables researchers to automatically acquire thousands of images over a matter of hours, and more. Contains a general overview on each topics that is followed by an in-depth presentation of a state-of-the-art approach Includes perspectives and content contributed by both technologists and biologists Covers specific problems of segmentation and mitosis detection Introduces the fundamentals of tracking and 3D analysis Presents open source data and toolsets for microscopy image analysis on an accompanying website

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
Author : Mei Chen
Publisher : Academic Press
Release Date : 2020-12-01
Category : Computers
Total pages :228
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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

Computer Vision and Machine Learning for Microscopy Image Analysis

Computer Vision and Machine Learning for Microscopy Image Analysis
Author : Carlos Federico Arteta
Publisher : Unknown
Release Date : 2015
Category :
Total pages :129
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Microscope Image Processing

Microscope Image Processing
Author : Qiang Wu,Fatima Merchant,Kenneth Castleman
Publisher : Elsevier
Release Date : 2010-07-27
Category : Computers
Total pages :576
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Digital image processing, an integral part of microscopy, is increasingly important to the fields of medicine and scientific research. This book provides a unique one-stop reference on the theory, technique, and applications of this technology. Written by leading experts in the field, this book presents a unique practical perspective of state-of-the-art microscope image processing and the development of specialized algorithms. It contains in-depth analysis of methods coupled with the results of specific real-world experiments. Microscope Image Processing covers image digitization and display, object measurement and classification, autofocusing, and structured illumination. Key Features: Detailed descriptions of many leading-edge methods and algorithms In-depth analysis of the method and experimental results, taken from real-life examples Emphasis on computational and algorithmic aspects of microscope image processing Advanced material on geometric, morphological, and wavelet image processing, fluorescence, three-dimensional and time-lapse microscopy, microscope image enhancement, MultiSpectral imaging, and image data management This book is of interest to all scientists, engineers, clinicians, post-graduate fellows, and graduate students working in the fields of biology, medicine, chemistry, pharmacology, and other related fields. Anyone who uses microscopes in their work and needs to understand the methodologies and capabilities of the latest digital image processing techniques will find this book invaluable. Presents a unique practical perspective of state-of-the-art microcope image processing and the development of specialized algorithms Each chapter includes in-depth analysis of methods coupled with the results of specific real-world experiments Co-edited by Kenneth R. Castleman, world-renowned pioneer in digital image processing and author of two seminal textbooks on the subject

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Release Date : 2017-01-18
Category : Computers
Total pages :458
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Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Image Technology

Image Technology
Author : Jorge L.C. Sanz
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
Category : Computers
Total pages :745
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Image processing and machine vision are fields of renewed interest in the commercial market. People in industry, managers, and technical engineers are looking for new technologies to move into the market. Many of the most promising developments are taking place in the field of image processing and its applications. The book offers a broad coverage of advances in a range of topics in image processing and machine vision.

A Study of Computer Vision and Pattern Recognition in Medical Image Analysis

A Study of Computer Vision and Pattern Recognition in Medical Image Analysis
Author : Jun Kong
Publisher : Unknown
Release Date : 2008
Category : Computer vision
Total pages :230
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Abstract: Computer vision and pattern recognition techniques have been fostered to solve many practical problems of diverse areas. Medical image analysis using machine vision and learning intelligence is one of the most sought-after fields. Computer vision addresses problems of the use of computers to detect, partition, represent, group, track, and interpret crucial primitives from given visual inputs. By contrast, pattern recognition is the study of distinguishing and recognizing different patterns represented with quantitative measurements. As a result, both of these two components usually present themselves in medical image analysis research work.

Analysis of Atomic Force Microscopy Images Using the Wavelet Transform

Analysis of Atomic Force Microscopy Images Using the Wavelet Transform
Author : Douglas Matthew Carmichael
Publisher : Unknown
Release Date : 2004
Category :
Total pages :238
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Scale-based Integrated Microscopic Computer Vision Techniques for Micromanipulation and Microassembly

Scale-based Integrated Microscopic Computer Vision Techniques for Micromanipulation and Microassembly
Author : Ge Yang
Publisher : Unknown
Release Date : 2004
Category :
Total pages :288
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Group and Crowd Behavior for Computer Vision

Group and Crowd Behavior for Computer Vision
Author : Vittorio Murino,Marco Cristani,Shishir Shah,Silvio Savarese
Publisher : Academic Press
Release Date : 2017-04-18
Category : Computers
Total pages :438
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Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. Presents the first book to cover the topic of modeling and analysis of groups in computer vision Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms Focuses on group and crowd analysis metrics Discusses real industrial systems dealing with the problem of analyzing groups and crowds

Computer-Assisted Microscopy

Computer-Assisted Microscopy
Author : John C. Russ
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
Category : Science
Total pages :470
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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.

Three-dimensional Computed Tomographic Image Analysis for Early Cancer Diagnosis in Small Pulmonary Nodules

Three-dimensional Computed Tomographic Image Analysis for Early Cancer Diagnosis in Small Pulmonary Nodules
Author : William Jason Kostis
Publisher : Unknown
Release Date : 2001
Category :
Total pages :230
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Modeling and Control of Biotechnical Processes 1992, (2nd IFAC Symposium) and Computer Applications in Fermentation Technology (5th International Conference)

Modeling and Control of Biotechnical Processes 1992, (2nd IFAC Symposium) and Computer Applications in Fermentation Technology (5th International Conference)
Author : Mohammed Nazmul Karim,G. Stephanopoulos
Publisher : Pergamon
Release Date : 1992
Category : Technology & Engineering
Total pages :482
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Hardbound. This volume provides the state-of-the-art findings of control theory and applications of biotechnical processes. Topics covered include neural networks and their applications, modeling, identification, AI and expert systems.

Computer Processing of Electron Microscope Images

Computer Processing of Electron Microscope Images
Author : P. W. Hawkes
Publisher : Springer
Release Date : 1980
Category : Computers
Total pages :296
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Towards the end of the 1960s, a number of quite different circumstances combined to launch a period of intense activity in the digital processing of electron micro graphs. First, many years of work on correcting the resolution-limiting aberrations of electron microscope objectives had shown that these optical impediments to very high resolution could indeed be overcome, but only at the cost of immense exper imental difficulty; thanks largely to the theoretical work of K. -J. Hanszen and his colleagues and to the experimental work of F. Thon, the notions of transfer func tions were beginning to supplant or complement the concepts of geometrical optics in electron optical thinking; and finally, large fast computers, capable of manipu lating big image matrices in a reasonable time, were widely accessible. Thus the idea that recorded electron microscope images could be improved in some way or rendered more informative by subsequent computer processing gradually gained ground. At first, most effort was concentrated on three-dimensional reconstruction, particu larly of specimens with natural symmetry that could be exploited, and on linear operations on weakly scattering specimens (Chap. l). In 1973, however, R. W. Gerchberg and W. O. Saxton described an iterative algorithm that in principle yielded the phase and amplitude of the electron wave emerging from a strongly scattering speci men.

New Trends in Image Analysis and Processing – ICIAP 2019

New Trends in Image Analysis and Processing – ICIAP 2019
Author : Marco Cristani,Andrea Prati,Oswald Lanz,Stefano Messelodi,Nicu Sebe
Publisher : Springer
Release Date : 2019-09-02
Category : Computers
Total pages :406
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This book constitutes the refereed proceedings of five workshops and an industrial session held at the 20th International Conference on Image Analysis and Processing, ICIAP 2019, in Trento, Italy, in September 2019: Second International Workshop on Recent Advances in Digital Security: Biometrics and Forensics (BioFor 2019); First International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2019); First International Workshop eHealth in the Big Data and Deep Learning Era (e-BADLE 2019); International Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments (DEEPRETAIL 2019); Industrial Session.