Pattern Recognition and Signal Analysis in Medical Imaging

Pattern Recognition and Signal Analysis in Medical Imaging
Author: Anke Meyer-Baese,Volker J. Schmid
Publsiher: Elsevier
Total Pages: 466
Release: 2014-03-21
Genre: Technology & Engineering
ISBN: 9780124166158

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Medical imaging is one of the heaviest funded biomedical engineering research areas. The second edition of Pattern Recognition and Signal Analysis in Medical Imaging brings sharp focus to the development of integrated systems for use in the clinical sector, enabling both imaging and the automatic assessment of the resultant data. Since the first edition, there has been tremendous development of new, powerful technologies for detecting, storing, transmitting, analyzing, and displaying medical images. Computer-aided analytical techniques, coupled with a continuing need to derive more information from medical images, has led to a growing application of digital processing techniques in cancer detection as well as elsewhere in medicine. This book is an essential tool for students and professionals, compiling and explaining proven and cutting-edge methods in pattern recognition for medical imaging. New edition has been expanded to cover signal analysis, which was only superficially covered in the first edition New chapters cover Cluster Validity Techniques, Computer-Aided Diagnosis Systems in Breast MRI, Spatio-Temporal Models in Functional, Contrast-Enhanced and Perfusion Cardiovascular MRI Gives readers an unparalleled insight into the latest pattern recognition and signal analysis technologies, modeling, and applications

Pattern Recognition for Medical Imaging

Pattern Recognition for Medical Imaging
Author: Anke Meyer-Bäse
Publsiher: Academic Press
Total Pages: 386
Release: 2004
Genre: Medical
ISBN: 9780124932906

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Essential tool for students and professionals that compiles and explains proven and cutting-edge methods in pattern recognition for medical imaging.

Medical Imaging

Medical Imaging
Author: K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publsiher: CRC Press
Total Pages: 238
Release: 2019-08-20
Genre: Computers
ISBN: 9780429642494

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The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Image Analysis and Recognition

Image Analysis and Recognition
Author: Fakhri Karray,Aurélio Campilho,Alfred Yu
Publsiher: Springer
Total Pages: 475
Release: 2019-08-12
Genre: Computers
ISBN: 9783030272029

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This two-volume set LNCS 11662 and 11663 constitutes the refereed proceedings of the 16th International Conference on Image Analysis and Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: Image Processing; Image Analysis; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep Learning on the Edge; Recognition; Applications; Medical Imaging and Analysis Using Deep Learning and Machine Intelligence; Image Analysis and Recognition for Automotive Industry; Adaptive Methods for Ultrasound Beamforming and Motion Estimation.

Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
Author: Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
Publsiher: Academic Press
Total Pages: 345
Release: 2018-11-30
Genre: Science
ISBN: 9780128160879

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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Biomedical Signal Analysis

Biomedical Signal Analysis
Author: Fabian J. Theis,Anke Meyer-Bäse
Publsiher: MIT Press
Total Pages: 415
Release: 2010
Genre: Computers
ISBN: 9780262013284

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A comprehensive introduction to innovative methods in the field of biomedical signal analysis, covering both theory and practice. Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmitting, analyzing, and displaying images have been developed in recent years, allowing scientists and physicians to obtain quantitative measurements to support scientific hypotheses and medical diagnoses. This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and interpretation.After an introduction to the topic and a survey of several processing and imaging techniques, the book describes a broad range of methods, including continuous and discrete Fourier transforms, independent component analysis (ICA), dependent component analysis, neural networks, and fuzzy logic methods. The book then discusses applications of these theoretical tools to practical problems in everyday biosignal processing, considering such subjects as exploratory data analysis and low-frequency connectivity analysis in fMRI, MRI signal processing including lesion detection in breast MRI, dynamic cerebral contrast-enhanced perfusion MRI, skin lesion classification, and microscopic slice image processing and automatic labeling. Biomedical Signal Analysis can be used as a text or professional reference. Part I, on methods, forms a self-contained text, with exercises and other learning aids, for upper-level undergraduate or graduate-level students. Researchers or graduate students in systems biology, genomic signal processing, and computer-assisted radiology will find both parts I and II (on applications) a valuable handbook.

Classification and Clustering in Biomedical Signal Processing

Classification and Clustering in Biomedical Signal Processing
Author: Dey, Nilanjan
Publsiher: IGI Global
Total Pages: 463
Release: 2016-04-07
Genre: Technology & Engineering
ISBN: 9781522501411

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Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.

Intelligent Computing in Signal Processing and Pattern Recognition

Intelligent Computing in Signal Processing and Pattern Recognition
Author: De-Shuang Huang,George William Irwin
Publsiher: Springer
Total Pages: 1182
Release: 2006-09-08
Genre: Technology & Engineering
ISBN: 9783540372585

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This 1179-page book assembles the complete contributions to the International Conference on Intelligent Computing, ICIC 2006: one volume of Lecture Notes in Computer Science (LNCS); one of Lecture Notes in Artificial Intelligence (LNAI); one of Lecture Notes in Bioinformatics (LNBI); and two volumes of Lecture Notes in Control and Information Sciences (LNCIS). Include are 149 revised full papers, and a Special Session on Computing for Searching Strategies to Control Dynamic Processes.

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
Author: Archana Mire,Vinayak Elangovan,Shailaja Patil
Publsiher: CRC Press
Total Pages: 168
Release: 2022-04-28
Genre: Technology & Engineering
ISBN: 9781000575958

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This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
Author: Xavier Pennec,Stefan Sommer,Tom Fletcher
Publsiher: Academic Press
Total Pages: 636
Release: 2019-09-02
Genre: Computers
ISBN: 9780128147269

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Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology Content includes: The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs Applications of statistics on manifolds and shape spaces in medical image computing Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. A complete reference covering both the foundations and state-of-the-art methods Edited and authored by leading researchers in the field Contains theory, examples, applications, and algorithms Gives an overview of current research challenges and future applications

Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
Author: Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
Publsiher: Academic Press
Total Pages: 345
Release: 2018-11-30
Genre: Science
ISBN: 9780128160879

Download Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book in PDF, Epub and Kindle

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Syntactic Pattern Recognition

Syntactic Pattern Recognition
Author: Flasinski Mariusz
Publsiher: World Scientific
Total Pages: 404
Release: 2019-03-25
Genre: Computers
ISBN: 9789813278486

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This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.

Pattern Recognition and Prediction with Applications to Signal Processing

Pattern Recognition and Prediction with Applications to Signal Processing
Author: David H. Kil,Frances B. Shin
Publsiher: American Institute of Physics
Total Pages: 418
Release: 1997-08-01
Genre: Computers
ISBN: 9781563964770

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Market: Engineers and researchers in neural networks, image processing, audio/speech, and medical imaging. This book begins by focusing on the theoretical aspect of pattern recognition and introduces an integrated pattern recognition paradigm, which combines preprocessing, low dimensional signal characterization, feature optimization, and mapping classifier architecture to good features in a seamless fashion. Later, the authors reinforce the concepts of pattern recognition and prediction with challenging real- world examples, encompassing financial market prediction, image coding, active and passive sonar processing, chaos modeling, and intelligent product design.

Computer Aided Intelligent Recognition Techniques and Applications

Computer Aided Intelligent Recognition Techniques and Applications
Author: Dr. Muhammad Sarfraz
Publsiher: John Wiley & Sons
Total Pages: 518
Release: 2005-11-01
Genre: Technology & Engineering
ISBN: 9780470094150

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Intelligent recognition methods have recently proven to be indispensable in a variety of modern industries, including computer vision, robotics, medical imaging, visualization and the media. Furthermore, they play a critical role in the traditional fields such as character recognition, natural language processing and personal identification. This cutting-edge book draws together the latest findings of industry experts and researchers from around the globe. It is a timely guide for all those require comprehensive, state-of-the-art advice on the present status and future potential of intelligent recognition technology. Computer-Aided Intelligent Recognition Techniques and Applications: Provides the user community with systems and tools for application in a very wide range of areas, including: IT, education, security, banking, police, postal services, manufacturing, mining, medicine, multimedia, entertainment, communications, data visualization, knowledge extraction, pattern classification and virtual reality. Disseminates information in a plethora of disciplines, for example pattern recognition, AI, image processing, computer vision and graphics, neural networks, cryptography, fuzzy logic, databases, evolutionary algorithms, shape and numerical analysis. Illustrates all theory with real-world examples and case studies. This valuable resource is essential reading for computer scientists, engineers, and consultants requiring up-to-date comprehensive guidance on the latest developments in computer-aided intelligent recognition techniques and applications. Its detailed, practical approach will be of interest to senior undergraduate and graduate students as well as researchers and industry experts in the field of intelligent recognition.

Recent Trends in Image Processing and Pattern Recognition

Recent Trends in Image Processing and Pattern Recognition
Author: K. C. Santosh,Ravindra S. Hegadi
Publsiher: Springer
Total Pages: 543
Release: 2019-07-15
Genre: Computers
ISBN: 9789811391842

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This three-book set constitutes the refereed proceedings of the Second International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2018, held in Solapur, India, in December 2018. The 173 revised full papers presented were carefully reviewed and selected from 374 submissions. The papers are organized in topical sections in the tree volumes. Part I: computer vision and pattern recognition; machine learning and applications; and image processing. Part II: healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications.