Spiculated Malignant Mass Detection and Classification in a Radiographic Image

摘要

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.

主权项

1. An anomaly detection system for identifying spiculated anomalies in an image comprising pixels, the system comprising:
a processor; and a non-transitory computer readable storage medium storing programming for execution by the processor, the programming including instructions for:
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, 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; anddetermining a dot product of the line mask image, the row component image, and the column component image with the directional vectors;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; andclassifying the selected ridge convergence hubs based on the extracted classification features.