Robust and automatic adjustment of display window width and center for MR images
AssigneeSiemens Corporate Research, Inc.
InventorsShang-Hong Lai, Ming Fang
Life of this patent
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Abstract
A system for deriving final display parameters for a wide range of MR images consists of a feature generator utilizing both histogram and spatial information computed from an input MR image, a wavelet transform within the feature generator for compressing the size of the feature vector, a competitive layer based neural network for clustering MR images into different subclasses, a bi-modal linear estimation network and a radial bases function network based non-linear estimator for each subclass, as well as a data fusion system using estimates from both estimators to compute the final display parameters.
Claims21
A system for the automatic adjustment of display window width and center for MR images comprising:
- a feature generator for receiving an input MR image;
- hierarchical neural networks having a classifier connected to said feature generator for clustering said input MR image into a plurality of subclasses and a bi-modal linear estimator for each of said subclasses, said hierarchical neural networks provide window width and center estimates;
- and
- data fusion connected to said hierarchical neural networks for combining said window width and center estimates for providing final display parameters.
A system for the automatic adjustment of display window width and center for MR images as claimed in claim I wherein said hierarchical neural networks further comprise
- a radial bases function estimator for each of said different subclasses connected to said classifier.
A method for automatically adjusting display window width and center for MR images comprising the steps of:
- utilizing a feature generator to receive an input MR image;
- clustering said input MR image into different subclasses with a clustering neural network;
- estimating each of said different subclasses with a bi-modal linear estimator;
- estimating each of said different subclasses with a radial bases function estimator;
- providing window width and center estimates from said clustering and estimating steps;
- and
- fusing data including said window width and center estimates from said bi-modal linear estimator and said radial bases function estimator to provide final display parameters.
Dependencies are inferred from the claim text.