Semi-automatic segmentation algorithm for pet oncology images
First Claim
1. A method for segmenting three-dimensional (3D) medical images containing a region of interest, the method comprising the steps of:
- identifying a first set of seed points within the region of interest;
identifying a second set of seed points outside the region of interest;
constructing a first sphere within the region of interest centered around the first set of seed points;
classifying voxels contained within 3D medical images using a spatial constrained fuzzy clustering algorithm whereby transforming the voxels contained within the 3D medical image into a fuzzy partition domain based on a homogeneity function;
successively generating a plurality of second spheres about said first sphere;
accepting ones of the plurality of second spheres that satisfy the homogeneity function threshold as defined by the spatial constricted fuzzy clustering algorithm;
adaptively growing a three-dimensional area defining the region of interest based on the step of accepting; and
displaying the region of interest defined by the step of adaptively growing.
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Abstract
An apparatus and method for segmenting three-dimensional (3D) medical images containing a region of interest is provided that identifies a first set of seed points within the region of interest and a second set of seed points outside the region of interest. A first sphere is constructed within the region of interest. Voxels contained within the medical image are classified using a spatial constrained fuzzy clustering algorithm. A plurality of second spheres is generated. Ones of the plurality of second spheres are accepted that satisfy the homogeneity function threshold as defined by the spatial constricted fuzzy clustering algorithm. A three-dimensional area is grown that defines the region of interest. The region of interest defined by the three-dimensional area is displayed.
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Citations
16 Claims
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1. A method for segmenting three-dimensional (3D) medical images containing a region of interest, the method comprising the steps of:
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identifying a first set of seed points within the region of interest; identifying a second set of seed points outside the region of interest; constructing a first sphere within the region of interest centered around the first set of seed points; classifying voxels contained within 3D medical images using a spatial constrained fuzzy clustering algorithm whereby transforming the voxels contained within the 3D medical image into a fuzzy partition domain based on a homogeneity function; successively generating a plurality of second spheres about said first sphere; accepting ones of the plurality of second spheres that satisfy the homogeneity function threshold as defined by the spatial constricted fuzzy clustering algorithm; adaptively growing a three-dimensional area defining the region of interest based on the step of accepting; and displaying the region of interest defined by the step of adaptively growing. - View Dependent Claims (2, 3, 4)
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5. A method for segmenting three-dimensional (3D) medical images containing a region of interest, the method comprising the steps of:
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identifying a first set of seed points within the region of interest; identifying a second set of seed points outside the region of interest; constructing a first sphere within the region of interest centered around the first set of seed points. classifying voxels contained within 3D medical images using a spatial constrained fuzzy clustering algorithm whereby transforming the voxels contained within the 3D medical image into a fuzzy partition domain based on a homogeneity function; successively generating a plurality of second spheres about said first sphere; accepting ones of the plurality of second spheres that satisfy the homogeneity function threshold as defined by the spatial constrained fuzzy clustering algorithm; adaptively growing a three-dimensional area defining the region of interest based on the step of accepting; selecting a radius of curvature of the first sphere and the plurality of second spheres to eliminate noise voxels on the interface between an inside of the region of interest and outside the region of interest; and displaying the region of interest defined by the steps of adaptively growing and selecting the radius of curvature. - View Dependent Claims (6, 7)
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8. A system for segmenting a medical image containing a region of interest and acquired by an image acquisition device, the system comprising:
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a processor coupled to the image acquisition device, the processor computing segmentation of the medical image wherein the computation of segmentation comprises the steps of; classifying voxels contained within the medical image using a spatial constrained fuzzy clustering algorithm as having a homogeneity function whereby transforming the voxels contained within the medical image to a fuzzy partition domain; constructing a first sphere within the region of interest wherein the first sphere is centered about a first set of seed points; successively generating a plurality of second spheres about said first sphere; accepting ones of the plurality of second spheres that satisfy the homogeneity function threshold as defined by the spatial constrained fuzzy clustering algorithm; adaptively growing a three-dimensional area defining the region of interest based on the step of accepting; and displaying the region of interest defined by the steps of adaptively growing; and an interface unit coupled to the processor for interpreting information relating to the segmentation of the medical image. - View Dependent Claims (9, 10, 11, 12)
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13. A system for segmenting medical images containing a region of interest and acquired by an image acquisition device, the system comprising:
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a processor coupled to the image acquisition device, the processor computing segmentation of the medical image wherein the computation of segmentation comprises the steps of; constructing a first sphere within the region of interest wherein the first sphere is centered about a first set of seed points;
classifying voxels contained within the medical image using a spatial constrained fuzzy clustering algorithm as having a homogeneity function whereby transforming the voxels contained within the medical image to a fuzzy partition domain;successively generating a plurality of second spheres about said first sphere; accepting ones of the plurality of second spheres that satisfy the homogeneity function threshold as defined by the spatial constrained fuzzy clustering algorithm; adaptively growing a three-dimensional area defining the region of interest based on the step of accepting; determining a radius of curvature of the first sphere and the plurality of second spheres to eliminate noise voxels on the interface between an inside of the region of interest and outside the region of interest; and displaying the region of interest defined by the steps of adaptively growing and selecting the radius of curvature; an interface unit coupled to the processor for interpreting information relating to the segmentation of the medical image. - View Dependent Claims (14, 15, 16)
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Specification