Medical Image Recognition, Segmentation And Parsing, Zhou,S. Kevin
Автор: Hati Название: Image Co-segmentation ISBN: 9811985693 ISBN-13(EAN): 9789811985690 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Описание: This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Описание: Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of several fusion strategie.- Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data.- Coronary Artery Centerline Refinement using GCN Trained with Synthetic Data.- Novel imaging biomarkers to evaluate heart dysfunction post-chemotherapy: a preclinical MRI feasibility study.- A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs.- Vessel Extraction and Analysis of Aortic Dissection.- The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images.- Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning.- Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders.- Improved AI-based Segmentation of Apical and Basal Slices from Clinical Cine CMR.- Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models.- Hierarchical multi-modality prediction model to assess obesity-related remodelling.- Neural Angular Plaque Characterization: Automated Quantification of Polar Distributionfor Plaque Composition.- Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography using Multi-task Learning.- Statistical shape analysis of the tricuspid valve in hypoplastic left heart syndrome.- An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment Correction in CardiacMR Imaging.- Unsupervised Multi-Modality RegistrationNetwork based on Spatially Encoded Gradient Information.- In-silico analysis of device-related thrombosis for different left atrial appendage occluder settings.- Valve flattening with functional biomarkers for the assessment of mitral valve repair.- Multi-modality cardiac segmentation via mixing domains for unsupervised adaptation.- Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction.- Cross-domain Artefact Correction of Cardiac MRI.- Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN.- Predicting 3D Cardiac Deformations With Point Cloud Autoencoders.- Influence of morphometric and mechanical factors in thoracic aorta finite element modeling.- Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease, Multi-View and Multi-Center.- Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI.- Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition.- Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation.- Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images.- Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model.- Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation.- Consistency based Co-Segmentation for Multi-View Cardiac MRI using Vision Transformer.- Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation.- A Multi-View Cross-Over Attention U-Net Cascade With Fourier Domain Adaptation For Multi-Domain Cardiac MRI Segmentation.- Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI using Efficient Late-Ensemble Deep Learning Approach.- Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.- 3D right ventricle reconstruction from 2D U-Net segmentation of sparse short-axis and 4-chamber cardiac cine MRI views.- Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation.- Using Out-of-Distribution Detection for Model Refinement in Cardiac Im
Описание: Advances in image-guided cancer surgery have enlarged the role of image segmentation and registration. This book reviews current methodologies, to help physicians delineate anatomical structures, enhance the accuracy of diagnosis and improve treatment planning.
Название: Biomedical Image Segmentation ISBN: 1482258552 ISBN-13(EAN): 9781482258554 Издательство: Taylor&Francis Рейтинг: Цена: 41342.00 р. Наличие на складе: Нет в наличии.
Автор: Jia Li; Robert M. Gray Название: Image Segmentation and Compression Using Hidden Markov Models ISBN: 1461370272 ISBN-13(EAN): 9781461370277 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs).
Автор: Zheng Название: Marginal Space Learning for Medical Image Analysis ISBN: 1493905996 ISBN-13(EAN): 9781493905997 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.
Автор: Meera Ramadas; Ajith Abraham Название: Metaheuristics for Data Clustering and Image Segmentation ISBN: 3030040968 ISBN-13(EAN): 9783030040963 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
Автор: Siddhartha Bhattacharyya; Paramartha Dutta; Sourav Название: Hybrid Soft Computing for Image Segmentation ISBN: 3319472224 ISBN-13(EAN): 9783319472225 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization.The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
Автор: Sourav De; Siddhartha Bhattacharyya; Susanta Chakr Название: Hybrid Soft Computing for Multilevel Image and Data Segmentation ISBN: 3319475231 ISBN-13(EAN): 9783319475233 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.
Автор: Siddiqui Название: Clustering Techniques for Image Segmentation ISBN: 3030812324 ISBN-13(EAN): 9783030812324 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM). The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. * Showcases major clustering techniques, detailing their advantages and shortcomings; * Includes several methods for evaluating the performance of segmentation techniques; * Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.
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