Statistical Shape And Deformation Analysis

Statistical Shape and Deformation Analysis

• Author : Guoyan Zheng
• Publisher : Academic Press
• Release Date : 2017-03-23
• Genre: Computers
• Pages : 508
• ISBN 10 : 9780128104941

Statistical Shape and Deformation Analysis: Methods, Implementation and Applications contributes enormously to solving different problems in patient care and physical anthropology, ranging from improved automatic registration and segmentation in medical image computing to the study of genetics, evolution and comparative form in physical anthropology and biology. This book gives a clear description of the concepts, methods, algorithms and techniques developed over the last three decades that is followed by examples of their implementation using open source software. Applications of statistical shape and deformation analysis are given for a wide variety of fields, including biometry, anthropology, medical image analysis and clinical practice. Presents an accessible introduction to the basic concepts, methods, algorithms and techniques in statistical shape and deformation analysis Includes implementation examples using open source software Covers real-life applications of statistical shape and deformation analysis methods

Statistical Shape Analysis

• Author : Ian L. Dryden
• Publisher : Wiley-Blackwell
• Release Date : 1998-09-16
• Genre: Mathematics
• Pages : 398
• ISBN 10 : UOM:39015048515913

Thos book involves methods for the geometrical study of random objects where location, rotation and scale information.

Statistical Shape Analysis

• Author : Ian L. Dryden
• Publisher : John Wiley & Sons
• Release Date : 2016-06-28
• Genre: Mathematics
• Pages : 496
• ISBN 10 : 9781119072508

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Statistical Shape Analysis

• Author : Ian L. Dryden
• Publisher : John Wiley & Sons
• Release Date : 2016-09-06
• Genre: Mathematics
• Pages : 508
• ISBN 10 : 9780470699621

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Intelligent Orthopaedics

• Author : Guoyan Zheng
• Publisher : Springer
• Release Date : 2018-10-10
• Genre: Medical
• Pages : 359
• ISBN 10 : 9789811313967

This book introduces readers to the latest technological advances in the emerging field of intelligent orthopaedics. Artificial intelligence and smart instrumentation techniques are now revolutionizing every area of our lives, including medicine. The applications of these techniques in orthopaedic interventions offer a number of potential benefits, e.g. reduced incision size and scarring, minimized soft tissue damage, and decreased risk of misalignment. Consequently, these techniques have become indispensable for various orthopaedic interventions, which has led to the emerging field of intelligent orthopaedics. Addressing key technologies and applications, this book offers a valuable guide for all researchers and clinicians who need an update on both the principles and practice of intelligent orthopaedics, and for graduate students embarking on a career in this field.​

Statistical Analysis of Shape and Deformation

• Author : Anonim
• Publisher : Unknown
• Release Date : 2011
• Genre: Uncategoriezed
• Pages : null
• ISBN 10 : OCLC:930625656

Statistical Atlases and Computational Models of the Heart Regular and CMRxMotion Challenge Papers

• Author : Oscar Camara
• Publisher : Springer Nature
• Release Date : 2023-01-27
• Genre: Computers
• Pages : 527
• ISBN 10 : 9783031234439

This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.

Riemannian Geometric Statistics in Medical Image Analysis

• Author : Xavier Pennec
• Publisher : Academic Press
• Release Date : 2019-09-02
• Genre: Computers
• Pages : 636
• ISBN 10 : 9780128147269

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,

Shape in Medical Imaging

• Author : Martin Reuter
• Publisher : Springer
• Release Date : 2018-11-22
• Genre: Computers
• Pages : 312
• ISBN 10 : 9783030047474

This book constitutes the proceedings of the Workshop on Shape in Medical Imaging, ShapeMI 2018, held in conjunction with the 21st International Conference on Medical Image Computing, MICCAI 2018, in Granada, Spain, in September 2018. The 26 full papers and 2 short papers presented were carefully reviewed and selected for inclusion in this volume. The papers discuss novel approaches and applications in shape and geometry processing and their use in research and clinical studies and explore novel, cutting-edge theoretical methods and their usefulness for medical applications, e.g., from the fields of geometric learning or spectral shape analysis.

Shape Analysis in Medical Image Analysis

• Author : Shuo Li
• Publisher : Springer Science & Business Media
• Release Date : 2014-01-28
• Genre: Technology & Engineering
• Pages : 442
• ISBN 10 : 9783319038131

This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as for example, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape analysis; machine learning for shape modeling and analysis; shape-based computer-aided-diagnosis; shape-based medical navigation; benchmark and validation of shape representation, analysis and modeling algorithms. This work will be of interest to researchers, students and manufacturers in the fields of artificial intelligence, bioengineering, biomechanics, computational mechanics, computational vision, computer sciences, human motion, mathematics, medical imaging, medicine, pattern recognition and physics.

Computer Vision for Microscopy Image Analysis

• Author : Mei Chen
• Publisher : Academic Press
• Release Date : 2020-12-01
• Genre: Computers
• Pages : 230
• ISBN 10 : 9780128149737

Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

Information Processing in Medical Imaging

• Author : Attila Kuba
• Publisher : Springer Science & Business Media
• Release Date : 1999-06-16
• Genre: Computers
• Pages : 525
• ISBN 10 : 9783540661672

The 1999 international conference on Information Processing in Medical Imaging (IPMI ’99) was the sixteenth in the series of biennial meetings and followed the successful meeting in Poultney, Vermont, in 1997. This year, for the rst time, the conference was held in central Europe, in the historical Hungarian town of Visegr ad, one of the most beautiful spots not only on the Danube Bend but in all Hungary. The place has many historical connections, both national and international. The castle was once a royal palace of King Matthias. In the middle ages, the Hungarian, Czech, and Polish kings met here. Recently, after the summit meeting of reestablished democracies in the area, it became a symbol for the cooperation between central European countries as they approached the European Union. It was thus also symbolic to bring IPMI, in the year of the 30th anniversary of its foundation, to this place, and organize the meeting with the close cooperation of local and traditional western organizers. It also provided a good opportunity to summarize brie?y a history of IPMI for those who were new to the IPMI conference. This year we received 82 full paper submissions from all over the world. Of these, 24 were accepted as oral presentations. These were divided into 6 sessions. In spite of our e orts, it was found to be impossible to make these sessions fully balanced and homogeneous.

Medical Image Computing and Computer Assisted Intervention MICCAI 2022

• Author : Linwei Wang
• Publisher : Springer Nature
• Release Date : 2022-09-15
• Genre: Computers
• Pages : 802
• ISBN 10 : 9783031164347

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

Permutation Tests in Shape Analysis

• Author : LUIGI SALMASO
• Publisher : Springer Science & Business Media
• Release Date : 2013-09-06
• Genre: Mathematics
• Pages : 95
• ISBN 10 : 9781461481638

Statistical shape analysis is a geometrical analysis from a set of shapes in which statistics are measured to describe geometrical properties from similar shapes or different groups, for instance, the difference between male and female Gorilla skull shapes, normal and pathological bone shapes, etc. Some of the important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate average shapes from a (possibly random) sample and to estimate shape variability in a sample[1]. One of the main methods used is principal component analysis. Specific applications of shape analysis may be found in archaeology, architecture, biology, geography, geology, agriculture, genetics, medical imaging, security applications such as face recognition, entertainment industry (movies, games), computer-aided design and manufacturing. This is a proposal for a new Brief on statistical shape analysis and the various new parametric and non-parametric methods utilized to facilitate shape analysis. ​ ​

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures

• Author : Hayit Greenspan
• Publisher : Springer Nature
• Release Date : 2019-10-10
• Genre: Computers
• Pages : 192
• ISBN 10 : 9783030326890

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.