Spiculated malignant mass detection and classification in radiographic image
First Claim
1. A method for identifying spiculated anomalies in an image comprising pixels, the method comprising:
- generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies;
detecting ridges in the digital image to generate a detected ridges map;
projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps;
determining wedge features for the potential convergence hubs from the set of ridge direction projection maps;
selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features;
extracting classification features for each of the selected ridge convergence hubs; and
classifying the selected ridge convergence hubs based on the extracted classification features,wherein projecting the detected ridges map onto the set of direction maps comprises;
separating the detected ridges map into a line mask image, a row component image, and a column component image; and
determining a dot product of the line mask image, the row component image, and the column component image with the directional vectors.
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Accused Products
Abstract
An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction) projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.
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Citations
18 Claims
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1. A method for identifying spiculated anomalies in an image comprising pixels, the method comprising:
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generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies; detecting ridges in the digital image to generate a detected ridges map; projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps; determining wedge features for the potential convergence hubs from the set of ridge direction projection maps; selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features; extracting classification features for each of the selected ridge convergence hubs; and classifying the selected ridge convergence hubs based on the extracted classification features, wherein projecting the detected ridges map onto the set of direction maps comprises; separating the detected ridges map into a line mask image, a row component image, and a column component image; and determining a dot product of the line mask image, the row component image, and the column component image with the directional vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for identifying spiculated anomalies in an image comprising pixels, the method comprising:
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a bulge mask generator generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies; a ridge detector detecting ridges in the digital image to generate a detected ridges map; a convergence projector projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps; a wedge feature calculator determining wedge features for the potential convergence hubs from the set of ridge direction projection maps; a convergence hub selector selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features; a feature extractor extracting classification features for each of the selected ridge convergence hubs; and a classifier classifying the selected ridge convergence hubs based on the extracted classification features, wherein the convergence projector separates the detected ridges map into a line mask image, a row component image, and a column component image, and determines a dot product of the line mask image, the row component image, and the column component image with the directional vectors. - View Dependent Claims (11, 12, 13)
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14. A computer program product for identifying spiculated anomalies in an image comprising pixels, the computer program product having a non-transitory computer-readable medium with a computer program embodied thereon, the computer program comprising:
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computer program code for generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies; computer program code for detecting ridges in the digital image to generate a detected ridges map; computer program code for projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps; computer program code for determining wedge features for the potential convergence hubs from the set of ridge direction projection maps; computer program code for selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features; computer program code for extracting classification features for each of the selected ridge convergence hubs; and computer program code for classifying the selected ridge convergence hubs based on the extracted classification features, wherein projecting the detected ridges map onto the set of direction maps comprises; separating the detected ridges map into a line mask image, a row component image, and a column component image; and determining a dot product of the line mask image, the row component image, and the column component image with the directional vectors. - View Dependent Claims (15, 16, 17, 18)
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Specification