System and method for determining convergence of image set registration
First Claim
1. A method for registering a plurality of image sets, wherein each of the plurality of image sets comprises at least one image, and wherein each of the plurality of image sets contain spatially overlapping areas of an imaged subject with at least one of the remaining plurality of the image sets, the method comprising:
- selecting a reference image set and an evaluation image set from said plurality of image sets, wherein the evaluation image set is to be aligned with the reference image set;
selecting a methodology for comparing of the registration between the reference image set and the evaluation image set;
selecting one or more point locations on the evaluation image set for tracking image movement;
selecting one or more fixed reference points for comparison with the one or more point locations on the evaluation image set;
selecting type of transformation to apply to the evaluation image set for aligning the evaluation image set with the reference image set;
a) calculating quality of alignment between the reference image set and the evaluation image set using a selected quality of alignment methodology;
b) calculating a location value (C) from one or more points on the evaluation image set with respect to the selected one or more fixed reference points and storing the calculation in a memory;
c) calculating a next transformation to apply to the evaluation image;
d) applying the transformation to at least a subset of the evaluation image set;
e) calculating a convergence value (V) for the current iteration (i), and storing the convergence value to the memory;
f) performing steps (a), (b) (c), (d) and (e) until at least a predetermined number N of correspondence calculation iterations have been performed; and
g) repeating steps (a), (b), (c), (d), and (e) if a total number of iterations (i) performed ≦
N and the convergence value (V)≦
(t), wherein (t) is a threshold value that is dynamically determined using a high-pass estimate of (V) to determine a noise level Ch(i).
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Abstract
Computer-based methods and systems for automatically determining convergence when registering image sets are provided. Example embodiments provide an Enhanced Image Registration System (EIRS), which includes an Image Comparison Module, a Transformation Optimizer, and a Convergence Calculator. When the EIRS receives two image sets to align, the Image Comparison Module compares two image sets to determine or measure how closely the image sets are aligned. The Transformation Optimizer determines an appropriate transformation to apply to one of the image sets to align it with the reference image set. The Transformation Optimizer then applies the determined transformation. The Convergence Calculator examines one or more points within the transformed image set to determine when convergence is attained.
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Citations
53 Claims
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1. A method for registering a plurality of image sets, wherein each of the plurality of image sets comprises at least one image, and wherein each of the plurality of image sets contain spatially overlapping areas of an imaged subject with at least one of the remaining plurality of the image sets, the method comprising:
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selecting a reference image set and an evaluation image set from said plurality of image sets, wherein the evaluation image set is to be aligned with the reference image set; selecting a methodology for comparing of the registration between the reference image set and the evaluation image set; selecting one or more point locations on the evaluation image set for tracking image movement; selecting one or more fixed reference points for comparison with the one or more point locations on the evaluation image set; selecting type of transformation to apply to the evaluation image set for aligning the evaluation image set with the reference image set; a) calculating quality of alignment between the reference image set and the evaluation image set using a selected quality of alignment methodology; b) calculating a location value (C) from one or more points on the evaluation image set with respect to the selected one or more fixed reference points and storing the calculation in a memory; c) calculating a next transformation to apply to the evaluation image; d) applying the transformation to at least a subset of the evaluation image set; e) calculating a convergence value (V) for the current iteration (i), and storing the convergence value to the memory; f) performing steps (a), (b) (c), (d) and (e) until at least a predetermined number N of correspondence calculation iterations have been performed; and g) repeating steps (a), (b), (c), (d), and (e) if a total number of iterations (i) performed ≦
N and the convergence value (V)≦
(t), wherein (t) is a threshold value that is dynamically determined using a high-pass estimate of (V) to determine a noise level Ch(i). - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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34. A system for registering a plurality of image sets, wherein each of the plurality of image sets comprises at least one image, and wherein each of the plurality of image sets contain spatially overlapping areas of an imaged subject with at least one of the remaining plurality of the image sets, the system comprising:
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means for selecting a reference image set and an evaluation image set from said plurality of image sets, wherein the evaluation image set is to be aligned with the reference image set; means for selecting a methodology for comparing the registration between the reference image set and the evaluation image set; means for selecting one or more point locations on the evaluation image set for tracking image alignment; means for selecting one or more fixed reference points for comparison with the one or more point locations on the evaluation image set; means for selecting type of transformation to apply to the evaluation image set for aligning the evaluation image set with the reference image set; a) means for calculating quality of alignment between the reference image set and the evaluation image set using the selected feature set; b) means for calculating a location value (C) from one or more points on the evaluation image set with respect to the selected one or more fixed reference points and storing the calculation in the memory; c) means for calculating a next transformation to apply to the evaluation image; d) applying the transformation to at least a subset of the evaluation image set; e) means for calculating a convergence value (V) for the current iteration (i), and storing the convergence value to the memory; f) means for performing steps (a), (b) (c), (d) and (e) until at least L5 correspondence calculation iterations have been performed wherein L5 is a minimum number of iterations before commencing the calculation of convergence; g) repeating steps (a), (b), (c), (d), and (e) if a total number of iterations (i) performed ≦
N and the convergence value (V)≦
(t), wherein (t) is a threshold value that is dynamically determined using a high-pass estimate of (V) to determine a noise level Ch(i). - View Dependent Claims (35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51)
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52. A software product capable of directing a general purpose computer to register a plurality of image sets, wherein each of the plurality of image sets contain spatially overlapping areas of an imaged subject with at least one of the remaining plurality of the image sets, the software product comprising:
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directing a general purpose computer to execute the steps of; selecting a reference image set and an evaluation image set from said plurality of image sets, wherein the evaluation image set is to be aligned with the reference image set; selecting a methodology for comparing the registration between the reference image set and the evaluation image set; selecting one or more point locations on the evaluation image set for tracking image alignment; selecting one or more fixed reference points for comparison with the one or more point locations on the evaluation image set; selecting type of transformation to apply to the evaluation image set for aligning the evaluation image set with the reference image set; a) calculating quality of alignment between the reference image set and the evaluation image set using the selected feature set; b) calculating a location value (C) from one or more points on the evaluation image set with respect to the selected one or more fixed reference points and storing the calculation in the memory; c) calculating a next transformation to apply to the evaluation image; d) applying the transformation to at least a subset of the evaluation image set; e) calculating a convergence value (V) for the current iteration (i), and storing the convergence value to the memory; f) performing steps (a), (b) (c), (d) and (e) until at least L5 correspondence calculation iterations have been performed; g) repeating steps (a), (b), (c), (d), and (e) if a total number of iterations (i) performed ≦
N and the convergence value (V)≦
(t), wherein (t) is a threshold value that is defined as t=f1+Ch(i)*f2.
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53. A system for registering image sets, the system comprising:
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a data processor, the data processor having a memory, wherein the data processor is capable of accessing and directing peripheral devices; a graphical user interface, wherein the graphical user interface is capable of interfacing with an navigating an imaging registration software product for registering image sets and wherein the software product is capable of instructing the data processor to perform instructions pursuant to the software product, the software product comprises; an instruction for selecting a reference image set and an evaluation image set from said plurality of image sets, wherein the evaluation image set is to be aligned with the reference image set; an instruction for selecting a methodology for comparing the registration between the reference image set and the evaluation image set; an instruction for selecting one or more point locations on the evaluation image set for tracking image alignment; an instruction for selecting one or more fixed reference points for comparison with the one or more point locations on the evaluation image set; an instruction for selecting type of transformation to apply to the evaluation image set for aligning the evaluation image set with the reference image set; a) an instruction for calculating quality of alignment between the reference image set and the evaluation image set using the selected feature set; b) an instruction for calculating a location value (C) from one or more points on the evaluation image set with respect to the selected one or more fixed reference points and storing the calculation in the memory; c) an instruction for calculating a next transformation to apply to the evaluation image; d) an instruction for applying the transformation to at least a subset of the evaluation image set; e) an instruction for calculating a convergence value (V) for the current iteration (i), and storing the convergence value to the memory; f) an instruction for performing steps (a), (b) (c), (d) and (e) until at least predetermined number of correspondence calculation iterations have been performed; g) an instruction for repeating steps (a), (b), (c), (d), and (e) if a total number of iterations (i) performed ≦
N and the convergence value (V)≦
(t), wherein (t) is a threshold value that is dynamically determined using a high-pass estimate of (V) to determine a noise level Ch(i).
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Specification