This article presents the Core Expansion method to automatically detect border-zone corridors in MRI images of the left ventricle, to serve as guidance to Ventricular Tachycardia (VT) ablation. The method relies on the fact that the different gray level intensities of Delayed Contrast-Enhanced Magnetic Resonance Images (DE-MRI) encode information about the fibrotic tissue. These differences in intensities among tissue types allow separating dense scar from healthy areas of the myocardium, and identify the border-zone region. After generating an onion-like layer-based 3D model of the left ventricle, the method detects potential corridors in the border-zone that can become electrical circuits of low conductivity. These circuits can be responsible for arrhythmic events. The method has been tested both in phantoms and patients. In patients there was a high degree of correlation between the channels detected and those visually identified by an expert on the MRI. Whenever electroanatomical maps were available post-intervention, the MRI detected channels were found to have a high degree of correlation with them.
This paper presents a new, highly flexible, scalable image coder
based on a Matching Pursuit expansion. The dictionary of atoms is
built by translation, rotation and anisotropic refinement of
gaussian functions, in order to efficiently capture edges in
natural images. In the same time, the dictionary is invariant
under isotropic scaling, which interestingly leads to very simple
spatial resizing operations. It is shown that the proposed scheme
compares to state-of-the-art coders when the compressed image is
transcoded to a lower (octave-based) spatial resolution. In
contrary to common compression formats, our bit-stream can
moreover easily and efficiently be decoded at any spatial
resolution, even with irrational re-scaling factors. In the same
time, the Matching Pursuit algorithm provides an intrinsically
progressive stream. This worthy feature allows for easy rate
filtering operations, where the least important atoms are simply
discarded to fit restrictive bandwidth constraints. Our scheme is
finally shown to favorably compare to state-of-the-art
progressive coders for moderate to quite important rate
reductions.
Digital data representation provides an efficient and fast way to access to information and to exchange it. In many situations though ownership or copyright protection mechanisms are desired. For still images and video, one possible way to achieve this is through watermarking. Watermarking consists of an imperceptible information embedded within a given media. Parallel Processing Watermarking Embedding Schemes have demonstrated to be efficient from a computational and memory usage point of view for very large images. These schemes consist in dividing the image into tiles and watermarking each independently. The processing allows the use of a parallel computation scheme. The watermarking method used in the scope of this work is a parallel variant of an approach known as self-referenced Spread Spectrum signature pattern. Since the watermarking scheme has been modified through tiling, the extra references due to signature replication can be used in the retrieval. This work describes the above mentioned approach to watermark images and provides an analysis of its performance.
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