Automatic abnormal tissue detection in MRI images
First Claim
1. A method for automatic material classification, comprising the steps of:
- acquiring a plurality of image sets of an object sequentially in time;
aligning the plurality of image sets with respect to spatial coordinates;
differencing the plurality of image sets with a reference image set to produce a plurality of difference image sets;
segmenting the plurality of difference image sets to produce a plurality of images with segmented intensity pixels;
applying dynamic system identification to the segmented intensity pixels of the plurality of images to produce a plurality of dynamic system parameters; and
classifying the plurality of system parameters into different classes.
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Abstract
A pattern recognition method for automatic abnormal tissue detection and differentiation using contrast enhanced MRI images. The method includes the steps of acquiring a plurality of MRI breast image sets; aligning the plurality of MRI breast images with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set, producing a plurality of difference image sets; segmenting the plurality of difference image sets, producing a plurality of MRI breast images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels, producing a plurality of dynamic system parameters; and classifying the plurality of system parameters augmented with other physical or non-physical factors into different classes.
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Citations
12 Claims
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1. A method for automatic material classification, comprising the steps of:
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acquiring a plurality of image sets of an object sequentially in time; aligning the plurality of image sets with respect to spatial coordinates; differencing the plurality of image sets with a reference image set to produce a plurality of difference image sets; segmenting the plurality of difference image sets to produce a plurality of images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels of the plurality of images to produce a plurality of dynamic system parameters; and classifying the plurality of system parameters into different classes. - View Dependent Claims (2)
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3. A method for abnormal tissue detection using contrast enhanced MRI images, comprising the steps of:
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acquiring a plurality of MRI breast image sets sequentially in time; aligning the plurality of MRI breast image sets with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set to produce a plurality of difference image sets; segmenting the plurality of difference image sets to produce a plurality of MRI breast image sets with segmented intensity pixels; applying a dynamic system identification to the segmented intensity pixels of the plurality of MRI breast image sets to produce a plurality of dynamic system parameters; and classifying the plurality of system parameters into different classes to detect abnormal tissue. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11)
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12. A pattern recognition method for abnormal tissue detection using contrast enhanced MRI images, comprising the steps of:
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acquiring a plurality of MRI breast image sets sequentially in time; aligning the plurality of MRI breast image sets with respect to spatial coordinates; generating a plurality of difference image sets by differencing the plurality of MRI breast image sets with a reference MRI image set; generating a plurality of MRI breast image sets with segmented intensity pixels by segmenting the plurality of difference image sets; producing a plurality of dynamic system parameters by applying a dynamic system identification to the segmented intensity pixels of the plurality of MRI breast image sets; and classifying the plurality of system parameters into different classes to detect abnormal tissue.
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Specification