Validation of Tissue Modelization and Classification Techniques in T1-Weighted MR Brain Images
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Remote sensing and geographic information science offer many pos- sibilities in terms of availability of diverse data. Some products like land cover layers or digital elevation models can be extracted from imagery and enable the realization of 3D city mode ...
Image registration is the concept of mapping homologous points in a pair of images. In other words, one is looking for an underlying deformation field that matches one image to a target image. The spectrum of applications of image registration is extremely ...
This paper shows that Hidden Markov Models (HMMs) can be effectively ap- plied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experi- ments conducted on the Face Recogn ...
In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: “Global Weighted Shape-Based Averaging” (GWSBA) and “Local Weig ...
Institute of Electrical and Electronics Engineers2013
Nuclear magnetic resonance (NMR) can be used in-vivo in a vast array of applications, such as anatomical imaging (magnetic resonance imaging, MRI), localized chemical composition characterization (magnetic resonance spectroscopy, MRS), cellular structure a ...
In this paper, we propose a Markov chain modeling of complicated phenomena observed from coupled chaotic oscillators. Once we obtain the transition probability matrix from computer simulation results, various statistical quantities can be easily calculated ...
Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analysis of focal lesions, manual or semi-a ...
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...
We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learn robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time- ...
In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps ...