Download or read book Biomedical Image or Genomic Data Characterization and Radiogenomic Image omics written by Ming Fan and published by Frontiers Media SA. This book was released on 2022-09-27 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Much of the emphasis in discussions about personalized medicine has been focused on the molecular characterization of tissue samples using microarray technology. However, as genetic differ between and within tumors and are quite heterogeneous, these techniques are limited. Imaging is noninvasive and is often used in routine clinical practice for disease diagnosis, treatment, and prognosis. Imaging is useful to guide disease therapy by providing a more comprehensive view of the entire lesion and it can be used on an ongoing basis to monitor lesion growth and progression or its response to treatment. The imaging includes but not limited to ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET). Radiomics refers to the conversion of images to high dimensional data and the subsequent mining for characterization of biology and ultimately to improve disease management for patients. Radiogenomics investigates relationships between imaging features and genomics, which represents the correlation between the anatomical-histological level to the genomic level. With advanced artificial intelligence methods, especially deep learning, data processing, feature extraction and data integration have been greatly improved. The topic is about artificial intelligence methods in biomedical images and genomics data for disease diagnosis, treatment, and prognosis, as listed here: • Biomarker identification from biomedical images to predict disease diagnosis, treatment, and prognosis • Radiogenomics/image-omics in identifying imaging biomarkers associated with molecular characteristics of the disease. • Machine learning/deep learning methods in biomedical imaging or genomics for disease detection and precision medicine. • Prediction of histological characteristics of disease based on biomedical imaging. • Integration of radiomics and genomics features for disease diagnosis, prognosis, and prediction medicine • Multimodality images or multi-omics data integration methods

Download or read book Big Data in Omics and Imaging written by MOMIAO. XIONG and published by CRC Press. This book was released on 2021-06-30 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases- from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

Download or read book Big Data in Omics and Imaging written by MOMIAO. XIONG and published by CRC Press. This book was released on 2021-06-30 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell -specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

Download or read book Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes written by Karâa, Wahiba Ben Abdessalem and published by IGI Global. This book was released on 2015-11-03 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.

Download or read book Handbook of Biomedical Image Analysis written by David Wilson and published by Springer Science & Business Media. This book was released on 2007-04-25 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a ?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks, ?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and ?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter ?rst reviews the applications, xi xii Preface challenges, and previous methods of image registration in the prostate.

Download or read book Biomedical Image Analysis and Machine Learning Technologies Applications and Techniques written by Gonzalez, Fabio A. and published by IGI Global. This book was released on 2009-12-31 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

Download or read book Biomedical Image Analysis written by Scott Thomas Acton and published by Morgan & Claypool Publishers. This book was released on 2006 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation Biomedical Image Analysis: Tracking addresses methods for extracting image information from biological/medical images for use in tracking biological targets. Here, and in the forthcoming companion Biomedical Image Analysis: Segmentation (Morgan & Claypool, ISBN: 1598290207), the authors concentrate on aspects of image analysis rather than the modalities or the imaging process itself. This lecture will be a valuable resource for graduate students, faculty, and industrial/governmental researchers interested in applications of imaging, or more specifically, biomedical imaging. It is written from first principles and will be accessible to a broad readership. Key Features:?Methods for tracking using active contours, together with a discussion of selection of parameters and weights?Methods for implementing snakes by way of dynamic programming?Probabilistic methods for tracking, with a description of the Kalman filter?Coverage of factored sampling and Monte Carlo methods, including the newly emerging particle filter?A summary of important new advances in target tracking, including multi-target tracking techniquesDescription of shape-based methods for biomedical image analysis.

Download or read book Graphs in Biomedical Image Analysis Computational Anatomy and Imaging Genetics written by M. Jorge Cardoso and published by Springer. This book was released on 2017-09-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.

Download or read book Biomedical Engineering Systems and Technologies written by Xuesong Ye and published by Springer Nature. This book was released on 2021-03-29 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes extended and revised versions of the selected papers from the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, held in Valletta, Malta, in February 2020. The 29 revised and extended full papers presented were carefully reviewed and selected from a total of 363 submissions. The papers are organized in topical sections on biomedical electronics and devices; bioimaging; bioinformatics models, methods and algorithms; bio-inspired systems and signal processing; health informatic

Download or read book Pattern Classification of Medical Images Computer Aided Diagnosis written by Xiao-Xia Yin and published by Springer. This book was released on 2017-06-27 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis. The analysis of time-series images is one of the most widely appearing problems in science, engineering, and business. In recent years this problem has gained importance due to the increasing availability of more sensitive sensors in science and engineering and due to the wide-spread use of computers in corporations which have increased the amount of time-series data collected by many magnitudes. An important feature of this book is the exploration of different approaches to handle and identify time dependent biomedical images. Biomedical imaging analysis and processing techniques deal with the interaction between all forms of radiation and biological molecules, cells or tissues, to visualize small particles and opaque objects, and to achieve the recognition of biomedical patterns. These are topics of great importance to biomedical science, biology, and medicine. Biomedical imaging analysis techniques can be applied in many different areas to solve existing problems. The various requirements arising from the process of resolving practical problems motivate and expedite the development of biomedical imaging analysis. This is a major reason for the fast growth of the discipline.

Download or read book Biomedical Information Technology written by David Dagan Feng and published by Academic Press. This book was released on 2019-10-22 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Information Technology, Second Edition, contains practical, integrated clinical applications for disease detection, diagnosis, surgery, therapy and biomedical knowledge discovery, including the latest advances in the field, such as biomedical sensors, machine intelligence, artificial intelligence, deep learning in medical imaging, neural networks, natural language processing, large-scale histopathological image analysis, virtual, augmented and mixed reality, neural interfaces, and data analytics and behavioral informatics in modern medicine. The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data. All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modeling, processing, registration, visualization, communication and large-scale biological computing. Presents the world's most recognized authorities who give their "best practices" Provides professionals with the most up-to-date and mission critical tools to evaluate the latest advances in the field Gives new staff the technological fundamentals and updates experienced professionals with the latest practical integrated clinical applications

Download or read book Radiomics and Radiogenomics written by Ruijiang Li and published by CRC Press. This book was released on 2019-07-09 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation

Download or read book Biomedical Texture Analysis written by Adrien Depeursinge and published by Academic Press. This book was released on 2017-08-25 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision Defines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators Showcases applications where biomedical texture analysis has succeeded and failed Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis

Download or read book Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume I written by Honghao Gao and published by Frontiers Media SA. This book was released on 2021-06-17 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Radiomics and Radiogenomics written by Ruijiang Li and published by CRC Press. This book was released on 2019-07-09 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation

Download or read book Handbook of Biomedical Image Analysis written by David Wilson and published by Springer. This book was released on 2005-06-09 with total page 2038 pages. Available in PDF, EPUB and Kindle. Book excerpt: With rapid advancements in technology, body imaging or components thereof, have become ubiquitous in medicine. While the biomedical devices such as the MRI, CT, X-rays, Ultrasound, PET/SPECT and Microscopy etc, provide us with high resolution images, the challenges that have continued to confront us with, lie in the interpretation of the vast amounts of data generated by these devices. Biomedical applications are the ‘bottom-line’ essentials in the diagnostic world. It is this diagnostic interpretation feature that forms the core niche for these books and will serve the needs of a broad spectrum of audience including researchers, research clinicians, and students. Together these three volumes of the Handbook of Biomedical Image Analysis, Volume 1 - Segementation Part A; Volume 2-Segmentation Part B; and Volume 3 - Registration, illustrate the role of the fusion of registration and segmentation systems for complete biomedical applications therapy delivery benefiting the biomedical doctors, clinical researchers, radiologists and others. Each volume in this set features a CD-ROM containing pedagogical material and numerous color illustrations.

Download or read book Biomedical Image Analysis written by Aly A. Farag and published by Cambridge University Press. This book was released on 2014-10-30 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.