Titlebook: Machine Learning in Medical Imaging; Second International Kenji Suzuki,Fei Wang,Pingkun Yan Conference proceedings 2011 Springer-Verlag Gmb

过分

Maximum Likelihood and James-Stein Edge Estimators for Left Ventricle Tracking in 3D Echocardiograplated structure of the endocardial boundary leads to alternating edge characteristics that varies over a cardiac cycle. The maximum gradient (MG), step criterion (STEP) and max flow/min cut (MFMC) edge detectors have been previously applied for the detection of endocardial edges. In this paper, we c

河潭

A Locally Deformable Statistical Shape Model,tures, they are often too constrained to capture the full amount of anatomical variation. This is due to the fact that the number of training samples is limited in general, because generating hand-segmented reference data is a tedious and time-consuming task. To circumvent this problem, we present a

Heart-Rate

Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Reshin the network clusters is modeled using a hidden Markov random field prior. The normalized time-series data, which lie on a high-dimensional sphere, are modeled with a mixture of von Mises-Fisher distributions. To estimate the parameters of this model, we maximize the posterior using a Monte Carlo

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DCE-MRI Analysis Using Sparse Adaptive Representations,nically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned wi

synchronous

Learning Optical Flow Propagation Strategies Using Random Forests for Fast Segmentation in Dynamic can aid in clinical diagnosis and disease assessment. We present an algorithm for automatic segmentation of the LV myocardium in 2D and 3D sequences which employs learning optical flow (OF) strategies. OF motion estimation is used to propagate single-frame segmentation results of the Random Forest c

BRIBE

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Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection,feature selection. The developed methodology was evaluated in a framework that supports the diagnosis of knee osteoarthritis (OA). Initially, a set of texture features are extracted from the MRI scans. These features are used for segmenting the region-ofinterest and as input to the PLS regression. O

arboretum

An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisationhe retinal image and the Gabor filter responses at four different scales are used as features for pixel classification. The Bayesian classifier is used with a bagging framework to classify each image pixel as vessel or background. A post processing method is also proposed to correct central reflex a