Titlebook: Medical Computer Vision; Recognition Techniqu Bjoern Menze,Georg Langs,Antonio Criminisi Conference proceedings 2011 Springer Berlin Heidel

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Conditional Point Distribution Modelsis technique is suited for sample-based segmentation methods that rely on a PDM, . [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs – often one or two landmarks are sufficient. The algorithm is easy to implement, h

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Deformable Registration of Organic Shapes via Surface Intrinsic Integrals: Application to Outer Ear established by means of a rich surface descriptor that incorporates three categories of features: (1) local and regional geometry; (2) surface anatomy; and (3) global shape information. First, surface intrinsic, ., are exploited to constrain the global geodesic layout. Consequently, the resulting tr

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Exploring Cortical Folding Pattern Variability Using Local Image Featuresta-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be pr

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良心

Motion Artifact Reduction in 4D Helical CT: Graph-Based Structure Alignmenteathing, images from different respiratory periods may be misaligned, thus the acquired 3D data may not accurately represent the anatomy. In this paper, we propose a method based on graph algorithms to reduce the magnitude of artifacts present in helical 4D CT images. The method strives to reduce th

Entirety

Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotay human experts, which may show considerable intra-rater and inter-rater variability. We experimentally evaluate several latent class and latent score models for tumor classification based on manual segmentations of different quality, using approximate variational techniques for inference. For the f

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Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimizationd matching based on discrete optimization. During training landmarks are annotated in a set of example volumes. A sparse elastic model encodes the geometric constraints of the landmarks. A Random Forest classifier learns the local appearance around the landmarks based on Haar-like 3D descriptors. Du

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Detection of 3D Spinal Geometry Using Iterated Marginal Space Learningination with Computed Tomography (CT) and Magnetic Resonance (MR) imaging. It is particularly important for the standardized alignment of the scan geometry with the spine. In this paper, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficien