MASS SEGMENTATION USING MIRROR IMAGE OF REGION OF INTEREST
First Claim
1. A method for segmenting an image representing a mass of a tissue, comprising:
- accessing digital image data representing an image including a tissue region;
extracting from said digital image data a Region of Interest (ROI) surrounding the mass;
transforming the ROI to polar space for obtaining a polar image of the ROI;
calculating an edge strength of the mass from said polar image;
calculating an expected gray level which corresponds with an edge of the mass by using Gaussian mixture;
calculating an expected mass radius by using data from said expected gray level;
assigning local cost to sub portions of said polar image as a weighted combination of the edge strength, expected gray level, and expected mass radius, wherein weight of the edge strength is larger in value than weight of the expected gray level; and
finding a contour of the mass based on the assigned local costs.
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Abstract
A method and an apparatus for automatic segmentation of an image representing a mass of a tissue region based on dynamic programming that guarantees an accurate and closed contour of the mass is disclosed. The method according to one embodiment accesses digital image data representing an image including the mass of the tissue region, creates a mirror image of the digital image data, extracts a Region of Interest (ROI) which includes a portion of the mirror image containing the mass, transforms the ROI to polar space for obtaining a polar image of the ROI, assigns local cost to sub portions of the polar image, and finds a contour of the mass based on the assigned local cost.
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Citations
17 Claims
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1. A method for segmenting an image representing a mass of a tissue, comprising:
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accessing digital image data representing an image including a tissue region; extracting from said digital image data a Region of Interest (ROI) surrounding the mass; transforming the ROI to polar space for obtaining a polar image of the ROI; calculating an edge strength of the mass from said polar image; calculating an expected gray level which corresponds with an edge of the mass by using Gaussian mixture; calculating an expected mass radius by using data from said expected gray level; assigning local cost to sub portions of said polar image as a weighted combination of the edge strength, expected gray level, and expected mass radius, wherein weight of the edge strength is larger in value than weight of the expected gray level; and finding a contour of the mass based on the assigned local costs. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An apparatus for automatic segmentation of an image representing a mass of a tissue region, comprising:
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an image data input unit for accessing digital image data representing an image including said mass of the tissue region; a Region of Interest (ROI) extraction unit for extracting a ROI surrounding the mass; an image transforming unit for transforming said ROI to polar space for obtaining a polar image of the ROI; an edge detection unit for calculating an edge strength of the mass from said polar image; a gray level calculation unit for calculating an expected gray level which corresponds with the edge of the mass by using Gaussian mixture; a mass radius calculation unit for calculating an expected mass radius by using data from said expected gray level; a local cost assignment unit for assigning local cost to sub portions of said polar image as a weighted combination of said edge strength, said expected gray level, and said expected mass radius, wherein the weight of the edge strength is larger in value than the weight of the expected gray scale; and a dynamic programming unit for finding a contour of the mass based on said assigned local cost. - View Dependent Claims (13, 14, 15, 16)
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17. A computer readable storage medium having stored thereon computer executable program for segmenting an image representing a mass of a tissue, the computer program when executed causes a processor to execute steps of:
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accessing digital image data representing an image including a tissue region; extracting from said digital image data a Region of Interest (ROI) surrounding the mass; transforming the ROI to polar space for obtaining a polar image of the ROI; calculating an edge strength of the mass from said polar image; calculating an expected gray level which corresponds with an edge of the mass by using Gaussian mixture; calculating an expected mass radius by using data from said expected gray level; assigning local cost to sub portions of said polar image as a weighted combination of the edge strength, expected gray level, and expected mass radius, wherein weight of the edge strength is larger in value than weight of the expected gray level; and finding a contour of the mass based on the assigned local costs.
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Specification