ROI selection in image registration
First Claim
1. A method of selecting an ROI (region of interest) within an image of an object, the method comprising:
- defining a measure H of the image, the measure H being suitable for registering the image with another image of the object;
maximizing the measure H; and
selecting the ROI by determining the region within the image in which the measure H is maximized.
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Accused Products
Abstract
A method and system is presented for automatically selecting a region of interest (ROI) within an image of an object, for example when performing non-rigid image registration between the image and another image of the object. In this way, the need for user interaction can be minimized or eliminated during image registration. The ROI is determined by defining an entropy measure H of the image, and selecting the region within the image in which the entropy measure is maximized, In this way, the ROI is optimized to contain as much information as possible. In one embodiment, the entropy measure H is a modified Shannon entropy, defined by H=−ΣI P(I) log P(I), where I is the value of the image intensity level, and P(I) is the probability of an image intensity value I occurring within the ROI.
201 Citations
47 Claims
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1. A method of selecting an ROI (region of interest) within an image of an object, the method comprising:
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defining a measure H of the image, the measure H being suitable for registering the image with another image of the object;
maximizing the measure H; and
selecting the ROI by determining the region within the image in which the measure H is maximized. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of selecting an ROI (region of interest) within a first image of an object so that the ROI can be registered with a second image of the object, the method comprising:
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a) calculating a measure H of the ROI; and
b) selecting an ROI within the first image in which the measure H is maximized. - View Dependent Claims (9, 10, 11)
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12. A system for selecting an ROI (region of interest) within an image of an object, the system comprising:
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a) a measure calculator configured to calculate a measure H of the image;
b) a measure maximizer configured to maximize the measure H; and
c) a controller configured to select an ROI within the image in which the measure H is maximized. - View Dependent Claims (13, 14)
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15. A system for selecting an ROI within a first image of an object so that image registration can be performed within the ROI between the first image and a second image of the object, the system comprising:
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a) an entropy calculator configured to calculate an entropy measure H of the first image;
b) an entropy maximizer configured to maximize the entropy measure H; and
c) a controller configured to select an ROI within the first image in which the entropy measure H is maximized. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A system for selecting an ROI within a first image of an object so that image registration can be performed within the ROI between the first image and a second image of the object, the system comprising:
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means for calculating an entropy measure H of the first image;
means for maximizing the entropy measure H; and
means for selecting an ROI within the first image in which the entropy measure H is maximized. - View Dependent Claims (23)
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24. A computer-readable medium having stored therein computer-readable instructions for a processor, wherein the instructions, when read and implemented by the processor, cause the processor to:
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input and store data representative of the intensity values of the pixels of an image;
calculate using the input data an entropy measure H of the image; and
select an ROI within the image in which H is maximized. - View Dependent Claims (25)
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26. An image registration system for registering at least one 2D image of an anatomical region with previously generated 3D scan data of the anatomical region, the anatomical region including at least one treatment target and at least one reference structure, wherein the 2D image is generated in near real time by detecting one or more radiographic imaging beams after the imaging beams have traversed at least a portion of the anatomical region, the imaging beams having known intensities and known positions and angles relative to the anatomical region, the system comprising:
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means for providing the 3D scan data of the anatomical region;
a scan data modifier configured to modify the 3D scan data so as to compensate for a difference between the ratio of bone-to-tissue attenuation at the energy level of the 3D scan, and the ratio of bone-to-tissue attenuation at the energy level of the imaging beam used for the near real-time 2D image;
a DRR generator configured to generate at least one DRR (digitally reconstructed radiograph) of the anatomical region, using the 3D scan data and the known locations, angles, and intensities of the imaging beams;
an ROI selector configured to select an ROI (region of interest) within the DRR, the ROI including the image of the treatment target and of the reference structure;
a motion field generator configured to generate a 3D full motion field within the ROI by estimating a plurality of local motion fields within the ROI; and
a parameter determiner configured to determine from the 3D full motion field a set of non-rigid transformation parameters that represent the difference in the position and orientation of the treatment target as shown in the 2D image, as compared to the position and orientation of the treatment target as shown in the DRR. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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39. A method of registering a near real time 2D x-ray image of an anatomical region with 3D scan data representative of a preoperative image of the anatomical region, the anatomical region including at least one reference structure and at least one treatment target, the method comprising:
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modifying the 3D scan data, and reconstructing from the modified 3D scan data at least one DRR;
selecting an ROI (region of interest) within the DRR, wherein the ROI includes at least one structure in the object;
generating a 3D motion field by estimating one or more 2D local motion fields within the ROI, and constructing a full 3D motion field from the local motion fields; and
determining from the full 3D motion field a set of non-rigid transformation parameters that represent the difference in the position and orientation of the reference structure and the treatment target as shown in the 2D x-ray image, as compared to the position and orientation of the reference structure and the treatment target as shown in the DRR. - View Dependent Claims (40, 41, 42, 43, 44, 45, 46, 47)
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Specification