Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging
Springer International Publishing (Verlag)
978-3-319-61187-7 (ISBN)
The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions.
The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images.
The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases.- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases.- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images.- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.- Inferring Disease Status by non-Parametric Probabilistic Embedding.- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images.- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker.- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation.- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images.- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features.- Representation Learning for Cross-Modality Classification.- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound.- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images.- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data.- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data.- Non-local Graph-based Regularization for Deformable Image Registration.- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation.
Erscheinungsdatum | 06.08.2017 |
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Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
Zusatzinfo | XIII, 222 p. 75 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 367 g |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Schlagworte | Applications • Artificial Intelligence • Bayesian networks • classification • Classifiers • Computer Science • computer vision • conference proceedings • Image Analysis • image enhancement • Image Processing • image processing and computer vision • image reconstruction • Image Registration • Image Segmentation • Imaging Systems • Informatics • Inverse Problems • Learning Systems • Markov Random Fields • Medical Images • Medical Imaging • Probability • Research • segmentation methods • Sensor Data Fusion • Support Vector Machines (SVM) |
ISBN-10 | 3-319-61187-9 / 3319611879 |
ISBN-13 | 978-3-319-61187-7 / 9783319611877 |
Zustand | Neuware |
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