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• Effects of stimuli intensity and frequency on auditory P50 na N100 sensory gating / Gaëlle Spielmann Moura, Yolanda Triñanes-Pego, and Maria T. Carrillo-de-la-Peña
• On buildng a memory evolutive system for applicatin to learning and cognition modeling / Julio de Lima do Rego Monteiro [and others]
• Agent-based cognitive model for human resources competence management / Stefan Oliveira and João Carlos Gluz
• Neural accumulator models of decision making in eye movements / Vassilis Cutsuridis
• On building meaning : a biologically-inspired experiment on symbol-based communication / Angelo Loula [and others]
• Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation / David Windridge and Josef Kittler
• Detection of auditory cortex activity by fMRI using a dependent component analysis / Carlos A. Estombelo-Montesco [and others]
• Brain-computer interface using wavelet transformation and naïve Bayes classsifier / Thiago Bassani and Julio Cesar Nievola
• Neuromorphic systems : past, present, and future / Leslie S. Smith
• Genetic algorithm applied to hierarchically coupled associative memories / Rogério Martins Gomes, Antônio Pádua Braga, and Henrique E. Borges
• Vector quantization of speech frames based on self-organizing maps / Flávio Olmos Simões [and others]
• The use of Bayesian networks for heart beat classification / Lorena Sophia Campos de Oliveira, Rodrigo Varejão Andreão, and Mário Sarcinelli-Filho
• A histogram based method for multiclass classification using SVMs / Sandro Tomassoni Coelho and Carlos Alberto Ynoguti
• Models of consciousness / Ron Chrisley and Rob Clowes
• A functional approach to emotion in autonomous systems / Ricardo Sanz [and others]
• A robot architecture based on higher order perception loop / Antonio Chella
• The consciousness circuit : an approach to the hard problem / Sulamita Frohlich and Carlos A. Franco
• Computational consciousness : building a self-preserving organism / Allan Kardec Barros
• The hippocampal system as the cortical resource manager : a model connecting psychology, anatomy, and physiology / L. Andrew Coward
• Cognitive measure on different profiles / Marilda Spindola [and others].

• Effects of stimuli intensity and frequency on auditory P50 na N100 sensory gating / Gae⁺{136}lle Spielmann Moura, Yolanda Trin⁺ёanes-Pego, and Maria T. Carrillo-de-la-Pen⁺ёa
• On buildng a memory evolutive system for applicatin to learning and cognition modeling / Julio de Lima do Rego Monteiro [and others]
• Agent-based cognitive model for human resources competence management / Stefan Oliveira and Joa⁺ёo Carlos Gluz
• Neural accumulator models of decision making in eye movements / Vassilis Cutsuridis
• On building meaning : a biologically-inspired experiment on symbol-based communication / Angelo Loula [and others]
• Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation / David Windridge and Josef Kittler
• Detection of auditory cortex activity by fMRI using a dependent component analysis / Carlos A. Estombelo-Montesco [and others]
• Brain-computer interface using wavelet transformation and nai⁺{136}ve Bayes classsifier / Thiago Bassani and Julio Cesar Nievola
• Neuromorphic systems : past, present, and future / Leslie S. Smith
• Genetic algorithm applied to hierarchically coupled associative memories / Rogério Martins Gomes, Antônio Pádua Braga, and Henrique E. Borges
• Vector quantization of speech frames based on self-organizing maps / Flávio Olmos Simo⁺ёes [and others]
• The use of Bayesian networks for heart beat classification / Lorena Sophia Campos de Oliveira, Rodrigo Vareja⁺ёo Andrea⁺ёo, and Mário Sarcinelli-Filho
• A histogram based method for multiclass classification using SVMs / Sandro Tomassoni Coelho and Carlos Alberto Ynoguti
• Models of consciousness / Ron Chrisley and Rob Clowes
• A functional approach to emotion in autonomous systems / Ricardo Sanz [and others]
• A robot architecture based on higher order perception loop / Antonio Chella
• The consciousness circuit : an approach to the hard problem / Sulamita Frohlich and Carlos A. Franco
• Computational consciousness : building a self-preserving organism / Allan Kardec Barros
• The hippocampal system as the cortical resource manager : a model connecting psychology, anatomy, and physiology / L. Andrew Coward
• Cognitive measure on different profiles / Marilda Spindola [and others].

Brain Inspired Cognitive Systems 2008 (June 24-27, 2008; Sao Luis, Brazil) brought together leading scientists and engineers who use analytic, syntactic and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods towards ever higher levels of cognitive competence. This book includes the papers presented at four major symposia: Part I - Cognitive Neuroscience Part II - Biologically Inspired Systems Part III - Neural Computation Part IV - Models of Consciousness.
(source: Nielsen Book Data)

• Technologies for implementing AmIHealth environments.- Frameworks related with AmIHealth environments.- Applied algorithms in e-Health systems.- Interactions within the AmIHealth environments.- Applications and case studies of AmIHealth environments.- Metrics for Health environments.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the First International Conference on Ambient Intelligence for Health, AmIHEALTH 2015, held in Puerto Varas, Chile, in December 2015. The 20 revised full papers and 9 short papers were reviewed and selected from 32 submissions and cover topics on technologies for implementing AmIHealth environments; frameworks related with AmIHealth environments; applied algorithms in e-Health systems; interactions within the AmIHealth environments; applications and case studies of AmIHealth environments; and metrics for health environments.
(source: Nielsen Book Data)

• Probabilistic Graphical Models
• Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach
• Template-Based Multimodal Joint Generative Model of Brain Data
• Generative Method to Discover Genetically Driven Image Biomarkers
• MRI Reconstruction A Joint Acquisition-Estimation Framework for MR Phase Imaging
• A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI
• Accelerated High Spatial Resolution Diffusion-Weighted Imaging
• Clustering
• Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI
• Joint Clustering and Component Analysis of Correspondenceless Point Sets: Application to Cardiac Statistical Modeling
• Statistical Methods
• Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations
• Controlling False Discovery Rate in Signal Space for Transformation-Invariant Thresholding of Statistical Maps
• Longitudinal Analysis
• Group Testing for Longitudinal Data
• Spatio-Temporal Signatures to Predict Retinal Disease Recurrence
• Microstructure Imaging
• A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI
• Ground Truth for Diffusion MRI in Cancer: A Model-Based Investigation of a Novel Tissue-Mimetic Material
• Shape Analysis
• Anisotropic Distributions on Manifolds: Template Estimation and Most Probable Paths
• A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes
• Multi-atlas Fusion Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data
• Keypoint Transfer Segmentation
• Fast Image Registration
• Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
• Fast Optimal Transport Averaging of Neuroimaging Data
• Deformation Models
• Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms
• A Robust Probabilistic Model for Motion Layer Separation in X-ray Fluoroscopy
• Poster Papers
• Weighted Hashing with Multiple Cues for Cell-Level Analysis of Histopathological Images
• Multiresolution Diffeomorphic Mapping for Cortical Surfaces
• A Comprehensive Computer-Aided Polyp Detection System for Colonoscopy Videos
• A Feature-Based Approach to Big Data Analysis of Medical Images
• Joint Segmentation and Registration Through the Duality of Congealing and Maximum Likelihood Estimate
• Self-Aligning Manifolds for Matching Disparate Medical Image Datasets
• Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in (k, q)-Space
• Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease
• Measuring Asymmetric Interactions in Resting State Brain Networks
• Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis
• Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging
• Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks
• Bodypart Recognition Using Multi-stage Deep Learning
• Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization
• Brain Transfer: Spectral Analysis of Cortical Surfaces and Functional Maps
• Finding a Path for Segmentation Through Sequential Learning
• Pancreatic Tumor Growth Prediction with Multiplicative Growth and Image-Derived Motion
• IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI
• Moving Frames for Heart Fiber Reconstruction
• Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors
• Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images
• A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data
• Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework
• Multi-scale Convolutional Neural Networks for Lung Nodule Classification
• Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex
• Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology
• Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series
• A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRI
• Towards a Quantified Network Portrait of a Population
• Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration
• AxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator
• Sampling from Determinantal Point Processes for Scalable Manifold Learning
• Model-Based Estimation of Microscopic Anisotropy in Macroscopically Isotropic Substrates Using Diffusion MRI
• Multiple Orderings of Events in Disease Progression
• Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech
• Tree-Encoded Conditional Random Fields for Image Synthesis
• Simultaneous Longitudinal Registration with Group-Wise Similarity Prior
• Spatially Weighted Principal Component Regression for High-Dimensional Prediction
• Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification
• Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI
• Functional Nonlinear Mixed Effects Models for Longitudinal Image Data.

This book constitutes the proceedings of the 24th International Conference on Information Processing in Medical Imaging, IPMI 2015, held at the Sabhal Mor Ostaig College on the Isle of Skye, Scotland, UK, in June/July 2015. The 22 full papers and 41 poster papers presented in this volume were carefully reviewed and selected from 195 submissions. They were organized in topical sections named: probabilistic graphical models; MRI reconstruction; clustering; statistical methods; longitudinal analysis; microstructure imaging; shape analysis; multi-atlas fusion; fast image registration; deformation models; and the poster session.

• Introduction.- Describing Spatial Position and Orientation.- Robot Kinematics.- Joint Velocities and Jacobi-Matrices.- Navigation and Registration.- Treatment Planning.- Motion Correlation and Tracking.- Motion Prediction.- Motion Replication.- Applications of Surgical Robotics.- Rehabilitation, Neuroprosthetics and Brain-Machine Interfaces.
• (source: Nielsen Book Data)

This book provides a thorough background to the emerging field of medical robotics. It covers the mathematics needed to understand the use of robotic devices in medicine, including but not limited to robot kinematics, hand-eye and robot-world calibration, reconstruction, registration, motion planning, motion prediction, motion correlation, motion replication and motion learning. Additionally, basic methods behind state-of-the art robots like the DaVinci system, the CyberKnife, motorized C-arms and operating microscopes as well as stereotactic frames are presented. The book is a text book for undergraduates in computer science and engineering. The main idea of the book is to motivate the methods in robotics in medical applications rather than industrial applications. The book then follows the standard path for a robotics textbook. It is thus suitable for a first course in robotics for undergraduates. It is the first textbook on medical robotics.
(source: Nielsen Book Data)

• A Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation
• Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image
• Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests
• Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis
• Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI
• Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling
• Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network
• Block-based Statistics for Robust Non-Parametric Morphometry
• Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection
• Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network
• An Effective Approach for Robust Lung Cancer Cell Detection
• Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation
• Hippocampus Segmentation through Distance Field Fusion
• Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing
• Fast Regions-of-Interest Detection in Whole Slide Histopathology Images
• Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation
• Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor
• Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression
• A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images
• 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method
• A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images
• Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques
• Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph
• Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework
• Efficient Multi-Scale Patch-based Segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015. The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data.

• Brain lesion image analysis
• Brain tumor image segmentation
• Ischemic stroke lesion image segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion (BrainLes), Brain Tumor Segmentation (BRATS) and Ischemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, on October 5, 2015, in conjunction with the International Conference on Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. The 25 papers presented in this volume were carefully reviewed and selected from 28 submissions. They are grouped around the following topics: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation.

• Brain Lesion.- Brain Tumor Segmentation (BRATS).- Ischemic Stroke Lesion Image Segmentation (ISLES), Mild Traumatic Brain Injury Outcome Prediction (mTOP).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.
(source: Nielsen Book Data)

• State-of-the-art techniques.- Novel and emerging analysis and visualization techniques.- Clinical challenges and open problems.- Major aspects of problems related to spine imaging.- Including clinical applications of spine imaging.- Computer aided diagnosis of spine conditions.- Computer aided detection of spine-related diseases.- Emerging computational imaging techniques for spinal diseases, .-Fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging.- Image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging.- Novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. They aim at reviewing the state-of-the-art techniques, sharing the novel and emerging analysis and visualization techniques and discussing the clinical challenges and open problems in this rapidly growing field - including all major aspects of problems related to spine imaging, including clinical applications of spine imaging, computer aided diagnosis of spine conditions, computer aided detection of spine-related diseases, emerging computational imaging techniques for spinal diseases, fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging, image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging, novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
(source: Nielsen Book Data)

• Active learning
• Semi-supervised learning
• Reinforcement learning
• Domain adaptation and transfer learning
• Crowd-sourcing annotations and fusion of labels from different sources
• Data augmentation
• Modelling of label uncertainty
• Visualization and human-computer interaction
• Image description
• Medical imaging-based diagnosis
• Medical signal-based diagnosis
• Medical image reconstruction and model selection using deep learning techniques
• Meta-heuristic techniques for fine-tuning
• Parameter in deep learning-based architectures
• Applications based on deep learning techniques.

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

• Medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain
• Data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues
• Computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.

This book constitutes the refereed proceedings of the 5th International Conference on Health Information Science, HIS 2016, held in Shanghai, China, in November 2016. The 13 full papers and 9 short papers presented were carefully reviewed and selected from numerous submissions. The scope of the papers includes medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.

• Introduction
• Image pre-processing
• Main Image processing
• Additional Image Processing and Measurement
• Impact of Image Acquisition and Selection of Algorithm Parameters on the Results
• Summary of Measured Features
• Conclusions.

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

• Ecare@Home: A Distributed Research Environment on Semantic Interoperability
• Halmstad Intelligent Home
• Capabilities and Opportunities
• Healthcare needs, company innovations, and research enabling solutions within Embedded Sensor Systems for Health
• A Case-Based Classification for Drivers? Alcohol Detection Using Physiological Signals
• Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
• A Classification Model for Predicting Heart Failure in Cardiac Patients by Using Unstructured Data
• Ins and Outs of Big Data: A Review
• A review of Parkinson?s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
• Why Hackers Love eHealth Applications
• Reliable Communication in Health Monitoring Applications11 Security Context Framework for Distributed Healthcare IoT Platform
• BitRun: Gamification of Health Data from Fitbit Activity Trackers
• Smartphone-based Decision Support System for Elimination of Pathology-free Images in Diabetic Retinopathy Screening
• Improving Awareness in Ambient-Assisted Living Systems: Consolidated Data Stream Processing
• Beyond?Happy Apps?: Using the Internet of Things to Support Emotional Health
• TEEM: a Mobile App for Technology-Enhanced Emergency Management
• Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
• Telemetry system for diagnosis of Asthma and Chronical Obstructive Pulmonary Disease (COPD)
• Remotely Supporting Patients with Obstructive Sleep Apnea at Home
• An aggregation platform for IoT-based healthcare: illustration for bioimpedancemetry, temperature and fatigue level monitoring.

This book constitutes the proceedings of the Third International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2016, held in Västerås, Sweden, October 18-19, 2016. The conference also included the First Workshop on Emerging eHealth through Internet of Things (EHIoT 2016). IoT as a set of existing and emerging technologies, notions and services provides many solutions to delivery of electronic healthcare, patient care, and medical data management. The 31 revised full papers presented along with 9 short papers were carefully reviewed and selected from 43 submissions in total. The papers cover topics such as healthcare support for the elderly, real-time monitoring systems, security, safety and communication, smart homes and smart caring environments, intelligent data processing and predictive algorithms in e-Health, emerging eHealth IoT applications, signal processing and analysis, and smartphones as a healthy thing.

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

• Brain analysis
• Brain analysis
• connectivity
• Brain analysis
• cortical morphology
• Alzheimer disease
• Surgical guidance and tracking
• Computer aided interventions
• Ultrasound image analysis
• cancer image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis; brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Machine learning and feature selection.- Deep learning in medical imaging.- Applications of machine learning.- Segmentation.- Cell image analysis.
• (source: Nielsen Book Data)

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.
(source: Nielsen Book Data)

• Registration and deformation estimation
• Shape modeling
• Cardiac and vascular image analysis
• Image reconstruction
• MR image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Part I Clinical Applications of Medical Imaging
• Part II Classification and Clustering- Part III Computer Aided Diagnosis (CAD) Tools and Case Studies
• Part-V Bio-inspiring Based Computer Aided Diagnosis Techniques.

This volume comprises of 21 selected chapters, including two overview chapters devoted to abdominal imaging in clinical applications supported computer aided diagnosis approaches as well as different techniques for solving the pectoral muscle extraction problem in the preprocessing part of the CAD systems for detecting breast cancer in its early stage using digital mammograms. The aim of this book is to stimulate further research in medical imaging applications based algorithmic and computer based approaches and utilize them in real-world clinical applications. The book is divided into four parts, Part-I: Clinical Applications of Medical Imaging, Part-II: Classification and clustering, Part-III: Computer Aided Diagnosis (CAD) Tools and Case Studies and Part-IV: Bio-inspiring based Computer Aided diagnosis techniques.

• Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning
• Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency
• Selective Labeling: identifying representative sub-volumes for interactive segmentation
• Robust and Accurate Appearance Models based on Joint Dictionary Learning: Data from the Osteoarthritis Initiative
• Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation
• Sparse-Based Morphometry: Principle and Application to Alzheimer's Disease
• Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning
• Patch-Based Discrete Registration of Clinical Brain Images
• Non-local MRI Library-based Super-resolution: Application to Hippocampus Subfield Segmentation
• Patch-based DTI grading: Application to Alzheimer's disease classification
• Hierarchical Multi-Atlas Segmentation using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement
• HIST: HyperIntensity Segmentation Tool
• Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-Atlas Segmentation
• CapAIBL: Automated reporting of cortical PET quantification without need of MRI on brain surface using a patch-based method
• High resolution hippocampus subfield segmentation using multispectral multi-atlas patch-based label fusion
• Identification of water and fat images in Dixon MRI using aggregated patch-based convolutional neural networks
• Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models.

This book constitutes the refereed proceedings of the Second International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2016, which was held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions. The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis.

• The use of technologies in favor of maintaining and improving mental wellbeing
• Building new computing paradigms
• Addressing a multitude of challenges in mental healthcare
• New technologies in psychiatric and psychological domains
• Video and audio technologies and mobile and wearable computing in mental healthcare.

This book constitutes the refereed proceedings of the 5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015, held in Milan, Italy, in September 2015. The 23 full papers and 6 short papers presented were carefully reviewed and selected from 40 submissions. The papers deal with the use of technologies in favor of maintaining and improving mental wellbeing. They focus on building new computing paradigms and on addressing a multitude of challenges in mental healthcare, for example in psychiatric and psychological domains with emphasis on new technologies, such as video and audio technologies and mobile and wearable computing.

• Fundamental methods for image-based biophysical modeling and image synthesis
• Biophysical and data-driven models of disease progression or organ development
• Biophysical and data-driven models of organ motion and deformation
• Biophysical and data-driven models of image formation and acquisition
• Segmentation/registration across or within modalities to aid the learning of model parameters
• Cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis
• Simulation and synthesis from large-scale image databases
• Automated techniques for quality assessment of simulations and synthetic images
• Image registration and segmentation
• Image denoising and information fusion
• Image reconstruction from sparse data or sparse views
• Real-time simulation of biophysical properties
• Simulation based approaches for medical imaging
• Synthesis and its applications in computational medical imaging.

This book constitutes the refereed proceedings of the First International Workshop on Simulation and Synthesis in Medical Imaging, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 revised full papers presented together in this book were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories: fundamental methods for image-based biophysical modeling and image synthesis; biophysical and data-driven models of disease progression or organ development; biophysical and data-driven models of organ motion and deformation; biophysical and data-driven models of image formation and acquisition; segmentation/registration across or within modalities to aid the learning of model parameters; cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis; simulation and synthesis from large-scale image databases; automated techniques for quality assessment of simulations and synthetic images; as well as several applications of image synthesis and simulation in medical imaging such as image registration and segmentation; image denoising and information fusion; image reconstruction from sparse data or sparse views; and real-time simulation of biophysical properties. The papers were divided into two general topics named "simulation based approaches for medical imaging" and "synthesis and its applications in computational medical imaging."