Computational Topology For Biomedical Image And Data Analysis

Computational Topology For Biomedical Image And Data Analysis Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Computational Topology For Biomedical Image And Data Analysis book. This book definitely worth reading, it is an incredibly well-written.

Computational Topology for Biomedical Image and Data Analysis

Author : Rodrigo Rojas Moraleda,Nektarios A. Valous,Wei Xiong,Niels Halama
Publisher : CRC Press
Page : 116 pages
File Size : 51,9 Mb
Release : 2019-07-12
Category : Medical
ISBN : 9780429810992

Get Book

Computational Topology for Biomedical Image and Data Analysis by Rodrigo Rojas Moraleda,Nektarios A. Valous,Wei Xiong,Niels Halama Pdf

This book provides an accessible yet rigorous introduction to topology and homology focused on the simplicial space. It presents a compact pipeline from the foundations of topology to biomedical applications. It will be of interest to medical physicists, computer scientists, and engineers, as well as undergraduate and graduate students interested in this topic. Features: Presents a practical guide to algebraic topology as well as persistence homology Contains application examples in the field of biomedicine, including the analysis of histological images and point cloud data

Research in Computational Topology

Author : Erin Wolf Chambers,Brittany Terese Fasy,Lori Ziegelmeier
Publisher : Springer
Page : 202 pages
File Size : 54,5 Mb
Release : 2018-07-30
Category : Mathematics
ISBN : 9783319895932

Get Book

Research in Computational Topology by Erin Wolf Chambers,Brittany Terese Fasy,Lori Ziegelmeier Pdf

Based on the first Workshop for Women in Computational Topology that took place in 2016, this volume assembles new research and applications in computational topology. Featured articles range over the breadth of the discipline, including topics such as surface reconstruction, topological data analysis, persistent homology, algorithms, and surface-embedded graphs. Applications in graphics, medical imaging, and GIS are discussed throughout the book. Four of the papers in this volume are the product of working groups that were established and developed during the workshop. Additional papers were also solicited from the broader Women in Computational Topology network. The volume is accessible to a broad range of researchers, both within the field of computational topology and in related disciplines such as statistics, computational biology, and machine learning.

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data

Author : Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardoso,Mustafa Hajij,Ghada Zamzmi,Paul Rahul,Lokendra Thakur
Publisher : Springer Nature
Page : 138 pages
File Size : 50,5 Mb
Release : 2021-09-21
Category : Computers
ISBN : 9783030874445

Get Book

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data by Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardoso,Mustafa Hajij,Ghada Zamzmi,Paul Rahul,Lokendra Thakur Pdf

This book constitutes the refereed joint proceedings of the 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, and the First International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021, held on September 27, 2021, in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021. The 7 full papers presented at iMIMIC 2021 and 5 full papers held at TDA4MedicalData 2021 were carefully reviewed and selected from 12 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. TDA4MedicalData is focusing on using TDA techniques to enhance the performance, generalizability, efficiency, and explainability of the current methods applied to medical data.

Medical Image Analysis

Author : Alejandro Frangi,Jerry Prince,Milan Sonka
Publisher : Academic Press
Page : 700 pages
File Size : 47,8 Mb
Release : 2023-09-20
Category : Technology & Engineering
ISBN : 9780128136584

Get Book

Medical Image Analysis by Alejandro Frangi,Jerry Prince,Milan Sonka Pdf

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

Biomedical Image Analysis

Author : Scott Acton,Nilanjan Ray
Publisher : Morgan & Claypool Publishers
Page : 116 pages
File Size : 47,9 Mb
Release : 2009-03-08
Category : Technology & Engineering
ISBN : 9781598290219

Get Book

Biomedical Image Analysis by Scott Acton,Nilanjan Ray Pdf

The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy platforms, image segmentation is a vital step in analyzing the constituent biological or medical targets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation that is accessible to well-equipped undergraduates, graduate students, and research professionals in the biology, biomedical, medical, and engineering fields. Active model methods that have emerged in the last few years are a focus of the book, including parametric active contour and active surface models, active shape models, and geometric active contours that adapt to the image topology. Additionally, Biomedical Image Analysis: Segmentation details attractive new methods that use graph theory in segmentation of biomedical imagery. Finally, the use of exciting new scale space tools in biomedical image analysis is reported. Table of Contents: Introduction / Parametric Active Contours / Active Contours in a Bayesian Framework / Geometric Active Contours / Segmentation with Graph Algorithms / Scale-Space Image Filtering for Segmentation

Advances in Computational Techniques for Biomedical Image Analysis

Author : Deepika Koundal,Savita Gupta
Publisher : Academic Press
Page : 324 pages
File Size : 53,8 Mb
Release : 2020-05-28
Category : Computers
ISBN : 9780128204115

Get Book

Advances in Computational Techniques for Biomedical Image Analysis by Deepika Koundal,Savita Gupta Pdf

Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation. Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems. Covers various challenges and common research issues related to biomedical image analysis Describes advanced computational approaches for biomedical image analysis Shows how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Explores a range of computational algorithms and techniques, such as neural networks, fuzzy sets, and evolutionary optimization Explores cloud based medical imaging together with medical imaging security and forensics

3D Image Reconstruction for CT and PET

Author : Daniele Panetta,Niccolo Camarlinghi
Publisher : CRC Press
Page : 135 pages
File Size : 44,9 Mb
Release : 2020-10-11
Category : Medical
ISBN : 9781000175820

Get Book

3D Image Reconstruction for CT and PET by Daniele Panetta,Niccolo Camarlinghi Pdf

This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

Author : M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer
Publisher : Springer
Page : 262 pages
File Size : 53,8 Mb
Release : 2017-09-06
Category : Computers
ISBN : 9783319676753

Get Book

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics by M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer Pdf

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.

Medical Physics During the COVID-19 Pandemic

Author : Kwan Hoong Ng,Magdalena S. Stoeva
Publisher : CRC Press
Page : 183 pages
File Size : 53,7 Mb
Release : 2021-03-28
Category : Science
ISBN : 9781000405934

Get Book

Medical Physics During the COVID-19 Pandemic by Kwan Hoong Ng,Magdalena S. Stoeva Pdf

The first book to cover the impact of COVID-19 on the field of medical physics Edited by two experts in the field, with chapter contributions from subject area specialists around the world Broad, global coverage, ranging from the impact on teaching, research, and publishing, with unique perspectives from journal editors and students and trainees

Machine Learning and Medical Imaging

Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Page : 512 pages
File Size : 50,8 Mb
Release : 2016-08-11
Category : Technology & Engineering
ISBN : 9780128041147

Get Book

Machine Learning and Medical Imaging by Guorong Wu,Dinggang Shen,Mert Sabuncu Pdf

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Computational Topology for Data Analysis

Author : Tamal Krishna Dey,Yusu Wang
Publisher : Cambridge University Press
Page : 455 pages
File Size : 50,7 Mb
Release : 2022-03-10
Category : Computers
ISBN : 9781009098168

Get Book

Computational Topology for Data Analysis by Tamal Krishna Dey,Yusu Wang Pdf

This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.

Biomedical Image Analysis

Author : Aly A. Farag
Publisher : Cambridge University Press
Page : 486 pages
File Size : 55,5 Mb
Release : 2014-10-30
Category : Computers
ISBN : 9780521196796

Get Book

Biomedical Image Analysis by Aly A. Farag Pdf

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.

Topological Data Analysis

Author : Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule
Publisher : Springer Nature
Page : 522 pages
File Size : 45,8 Mb
Release : 2020-06-25
Category : Mathematics
ISBN : 9783030434083

Get Book

Topological Data Analysis by Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule Pdf

This book gathers the proceedings of the 2018 Abel Symposium, which was held in Geiranger, Norway, on June 4-8, 2018. The symposium offered an overview of the emerging field of "Topological Data Analysis". This volume presents papers on various research directions, notably including applications in neuroscience, materials science, cancer biology, and immune response. Providing an essential snapshot of the status quo, it represents a valuable asset for practitioners and those considering entering the field.

Topology in Real-World Machine Learning and Data Analysis

Author : Kathryn Hess,Frédéric Chazal,Umberto Lupo
Publisher : Frontiers Media SA
Page : 229 pages
File Size : 46,6 Mb
Release : 2022-11-07
Category : Science
ISBN : 9782832504123

Get Book

Topology in Real-World Machine Learning and Data Analysis by Kathryn Hess,Frédéric Chazal,Umberto Lupo Pdf

Computational Topology in Image Context

Author : Alexandra Bac,Jean-Luc Mari
Publisher : Springer
Page : 303 pages
File Size : 47,6 Mb
Release : 2016-06-01
Category : Computers
ISBN : 9783319394411

Get Book

Computational Topology in Image Context by Alexandra Bac,Jean-Luc Mari Pdf

This book constitutes the proceedings of the 6th International Workshop on Computational Topology in Image Context, CTIC 2016, held in Marseille, France, in June 2016. The 24 papers presented in this volume were carefully reviewed and selected from 35 submissions. Additionally, this volume contains 2 invited papers. CTIC covers a wide range of topics such as: topological invariants and their computation, homology, cohomology, linking number, fundamental groups; algorithm optimization in discrete geometry, transfer of mathematical tools, parallel computation in multi-dimensional volume context, hierarchical approaches; experimental evaluation of algorithms and heuristics; combinatorial or multi-resolution models; discrete or computational topology; geometric modeling guided by topological constraints; computational topological dynamics; and use of topological information in discrete geometry applications.