Computational Topology For Biomedical Image And Data Analysis

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

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.

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 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

This collection of peer-reviewed workshop papers provides comprehensive coverage of cutting-edge research into topological approaches to data analysis and visualization. It encompasses the full range of new algorithms and insights, including fast homology computation, comparative analysis of simplification techniques, and key applications in materials and medical science. The book also addresses core research challenges such as the representation of large and complex datasets, and integrating numerical methods with robust combinatorial algorithms. In keeping with the focus of the TopoInVis 2017 Workshop, the contributions reflect the latest advances in finding experimental solutions to open problems in the sector. They provide an essential snapshot of state-of-the-art research, helping researchers to keep abreast of the latest developments and providing a basis for future work. Gathering papers by some of the world’s leading experts on topological techniques, the book represents a valuable contribution to a field of growing importance, with applications in disciplines ranging from engineering to medicine.

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

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

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