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We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms.
CNN utilizes the image appearance pattern around the lymph node.
Since it displays only the maximum signal intensity, calcification will dominate the image appearance.
Combined with increased voxel correlation, this reduction of voxel variance alters the image appearance quite considerably and may be perceived as over-smoothing of images.
To illustrate the discriminative power of the image appearance pattern, we extracted the intermediate feature vector produced by the internal flattening layer of the CNN.
This technique uses deep artificial neural networks to learn the image appearance patterns of interested objects based on a large training data set.
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The criteria of comparison are (1) contrast enhancement and (2) the extent to which each algorithm preserves the original image appearance (in the sense that it should not produce any new details or structure on the image) without introducing unwanted artefacts.
Results show the effectiveness of the proposed algorithm in enhancing local and global contrasts, suppressing saturation and over-enhancement artefacts while retaining the original image appearance.
The proposed method balances the requirement of local and global contrast enhancements and a faithful representation of the original image appearance, an objective that is difficult to achieve using traditional enhancement methods.
The developed framework requires annotated training data (atlases), which are used both for atlas registration, and for training the statistical image appearance models.
The developed framework utilizes twenty manually outlined hippocampi (atlases) [ 44], which are used both for atlas registration, and for training the statistical image appearance models.
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