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Complex Systems Science in Biomedicine
Complex Systems Science in Biomedicine covers the emerging field of systems science involving the application of physics, mathematics, engineering and computational methods and techniques to the study of biomedicine including nonlinear dynamics at the molecular, cellular, multi-cellular tissue, and organismic level. With all chapters helmed by leading scientists in the field, Complex Systems Science in Biomedicine's goal is to offer its audience a timely compendium of the ongoing research directed to the understanding of biological processes as whole systems instead of as isolated component parts.
Classic Papers in Modern Diagnostic Radiology
The subject of diagnostic radiology is now very large and radiology depa- ments are involved in all areas of modern patient care.The defining event in m- ern radiology,and arguably the most significant development in radiology since Wilhelm Röntgen discovered X-rays, was the invention of the CT scanner in the 1970s.The CT scanner introduced modern cross-sectional imaging and also di- tal imaging.We now have MRI and ultrasound and these techniques are replacing many traditional X-ray procedures.The developments in radiology have been the result of a fruitful interaction between the basic sciences, clinical medicine and the manufacturers. This can be seen by looking at the various sources of these publications. Change is produced by the interactions between the various dis- plines. The editors have had a very difficult task in selecting the key discoveries and descriptions.The radiological literature is very large.Medical imaging continues to develop rapidly and these papers are the foundations of our current practice.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
Artificial neural networks : Recent advances, new perspectives and applications
This book explores the potential of ANNs for applications in different fields. Itincludes eight chapters that discuss deep learning, ANN tools, and other cutting-edgetechnologies. It also suggests avenues for further research into ANN techniques formedical imaging to detect breast tumors, classification of COVID-19 surveillancedatasets, health management, estimation of materials processing parameters, solarenergy management, and control of a petrochemical unit.
Artificial intelligence applied to medical imaging and computational biology
Medical imaging and computational biology continuously pose new fundamental medical and biological questions that often give rise to novel challenges in Artificial Intelligence. These research fields present an increasing need for the application of cutting-edge computational approaches that generally involve machine learning or computational intelligence techniques, which can effectively perform bioimage and biosignal processing in different clinical areas.
Anisotropy Across Fields and Scales
This book focuses on processing, modeling, and visualization of anisotropy information…
Alternative breast imaging : Four model-based approaches
Medical imaging has been transformed over the past 30 years by the advent of computerized tomography (CT), magnetic resonance imaging (MRI), and various advances in x-ray and ultrasonic techniques. An enabling force behind this progress has been the (so far) exponentially increasing power of computers, which has made it practical to explore fundamentally new approaches. In particular, what our group terms "model-based" modalities-which produce tissue property images from data using nonlinear, iterative numerical modeling techniques-have become increasingly feasible. Alternative Breast Imaging: Four Model-Based Approaches explores our research on four such modalities, particularly with regard to imaging of the breast: (1) MR elastography (MRE), (2) electrical impedance spectroscopy (EIS), (3) microwave imaging spectroscopy (MIS), and (4) near infrared spectroscopic imaging (NIS).
Mathematical Models for Registration and Applications to Medical Imaging
Image registration is an emerging topic in image processing with many applications in medical imaging, picture and movie processing. The classical problem of image registration is concerned with ?nding an appropriate transformation between two data sets. This fuzzy de?nition of registration requires a mathematical modeling and in particular a mathematical speci?cation of the terms appropriate transformations and correlation between data sets. Depending on the type of application, typically Euler, rigid, plastic, elastic deformations are considered. The variety of similarity p measures ranges from a simpleL distance between the pixel values of the data to mutual information or entropy distances. This goal of this book is to highlight by some experts in industry and medicine relevant and emerging image registration applications and to show new emerging mathematical technologies in these areas. Currently, many registration application are solved based on variational prin- ple requiring sophisticated analysis, such as calculus of variations and the theory of partial differential equations, to name but a few. Due to the numerical compl- ity of registration problems ef?cient numerical realization are required. Concepts like multi-level solver for partial differential equations, non-convex optimization, and so on play an important role. Mathematical and numerical issues in the area of registration are discussed by some of the experts in this volume.
Biophotonics ; Optical science and engineering for the 21st century
Biophotonics: Optical Science and Engineering in the 21st Century serves as an ideal aid to the research and development of these areas integrating light, photonics, and biological systems.Key topics include: Fluctuation Correlation Spectroscopy in Cells: Determination of Molecular Aggregation ,Using GFP and FRET Technologies for Studying Signaling Mechanisms of Apoptosis in a Single Living Cell, Study on Protein-Protein Interaction in Single Living Cells, Functional Optical Coherence Tomography: Simultaneous In Vivo Imaging of Tissue Structure and Physiology, Imaging –Photo- and Sonodynamic Diagnosis of Cancer Mediated by Chemiluminescence Probes, Biophotonic Analysis of Spontaneous Self-Organizing Oxidative Processes in Aqueous Systems, Biophoton Emission and Defense Systems in Plants
An Introduction to Mathematics of Emerging Biomedical Imaging
Biomedical imaging is a fascinating research area to applied mathematicians. Challenging imaging problems arise and they often trigger the investigation of fundamental problems in various branches of mathematics. This is the first book to highlight the most recent mathematical developments in emerging biomedical imaging techniques. The main focus is on emerging multi-physics and multi-scales imaging approaches. For such promising techniques, it provides the basic mathematical concepts and tools for image reconstruction. Further improvements in these exciting imaging techniques require continued research in the mathematical sciences, a field that has contributed greatly to biomedical imaging and will continue to do so.
Advances in Medical Engineering
In this book, research and development trends of physics, engineering, mathematics and computer sciences in biomedical engineering are presented. Contributions from industry, clinics, universities and research labs with foci on medical imaging (CT, MRT, US, PET, SPECT etc.), medical image processing (segmentation, registration, visualization etc.), computer-assisted surgery (medical robotics, navigation), biomechanics (motion analysis, accident research, computer in sports, ergonomics etc.), biomedical optics (OCT, soft-tissue optics, optical monitoring etc.) and laser medicine (tissue ablation, gas analytics, topometry etc.) give insight to recent engineering, clinical and mathematical studies.
4th Kuala Lumpur International Conference on Biomedical Engineering 2008 ; BIOMED 2008 25–28 June 2008 Kuala Lumpur, Malaysia
The topics covered in the conference proceedings include: Artificial organs, bioengineering education, bionanotechnology, biosignal processing, bioinformatics, biomaterials, biomechanics, biomedical imaging, biomedical instrumentation, bioMEMS, clinical engineering, prosthetics.
