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Titlebook: Advanced Computational Approaches to Biomedical Engineering; Punam K. Saha,Ujjwal Maulik,Subhadip Basu Book 2014 Springer-Verlag Berlin He

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發(fā)表于 2025-3-21 16:41:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advanced Computational Approaches to Biomedical Engineering
影響因子2023Punam K. Saha,Ujjwal Maulik,Subhadip Basu
視頻videohttp://file.papertrans.cn/146/145322/145322.mp4
發(fā)行地址Reference for graduate students, researchers and industrial practitioners in computer science, biomedical engineering and computational and molecular biology.Focuses on intelligent techniques for anal
圖書封面Titlebook: Advanced Computational Approaches to Biomedical Engineering;  Punam K. Saha,Ujjwal Maulik,Subhadip Basu Book 2014 Springer-Verlag Berlin He
影響因子.There has been rapid growth in biomedical engineering in recent decades, given advancements in medical imaging and physiological modelling and sensing systems, coupled with immense growth in computational and network technology, analytic approaches, visualization and virtual-reality, man-machine interaction and automation. Biomedical engineering involves applying engineering principles to the medical and biological sciences and it comprises several topics including biomedicine, medical imaging, physiological modelling and sensing, instrumentation, real-time systems, automation and control, signal processing, image reconstruction, processing and analysis, pattern recognition, and biomechanics. It holds great promise for the diagnosis and treatment of complex medical conditions, in particular, as we can now target direct clinical applications, research and development in biomedical engineering is helping us to develop innovative implants and prosthetics, create new medical imaging technologies and improve tools and techniques for the detection, prevention and treatment of diseases..The contributing authors in this edited book present representative surveys of advances in their respe
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Being and Contemporary Psychoanalysisthe rich structure present in the observed images. We review the most important problems encountered in the processes of analysis of fluorescent confocal microscopy images. Each of these problems suggests a dedicated computational approach.
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Book 2014g systems, coupled with immense growth in computational and network technology, analytic approaches, visualization and virtual-reality, man-machine interaction and automation. Biomedical engineering involves applying engineering principles to the medical and biological sciences and it comprises seve
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Being an Interdisciplinary Academicdict the biological function for a given biomolecule using multiscale features, and more generally to model a meta-learning prediction system to analyze the impact of micro-dynamics on global behavior for selected biological systems, with important roles in chemistry, biology, and medicine.
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https://doi.org/10.1007/978-3-030-18476-6nsight into the pathogenesis of osteoarthritis and may permit early diagnosis of osteoarthritis in at-risk subjects. This knowledge and capability should ultimately accelerate the discovery and testing of novel therapies to treat osteoarthritis, a disease which represents an enormous socioeconomic and health burden on society.
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https://doi.org/10.1057/978-1-137-54325-7of-the-artgraph-based methods for segmenting three-dimensional structures in medical images: graph cuts and the Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces (LOGISMOS) approach. In each case, an overview of the underlying optimization problem is presented first (i.e., th
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Being an Expert Professional Practitionercal changes. Major computational efforts are essential in unsupervised learning of structures in various medical images. Some unsupervised learning techniques that take advantage of information theory concepts provide a different perspective on the solution of automated learning methods. This chapte
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Expertise: The Relational Turn,of decades. In this regard, unsupervised clustering methods are used to group the pixels of images in the intensity space. Most of these clustering methods optimize a single clustering measure. However, to improve the clustering further multiple conflicting clustering measures can be used. In this c
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