Identifying image abnormalities using an appearance model

摘要

The identification of known normal structures within an image is preferably accomplished using an appearance model. Specifically, an active appearance model, which encapsulates a complete model of the shape and global texture variations of an object from a collection of samples, is utilized to define normal structures within an image by restricting training samples supplied to the active appearance model during a training phase to those that do not contain abnormal structures. Accordingly, the trained appearance model represents only normal variations in the object of interest. When another image with abnormalities is presented to the system, the appearance model cannot synthesize the abnormal structures which show up as errors in a residual image. Accordingly, the errors in the residual image represent potential abnormalities.

主权项

1. A method of detecting abnormalities in an input image of an object, the method comprising:
receiving the input image of the object at a processing system; receiving, at the processing system, a sample normal image of a normal object formed using an appearance model, wherein the appearance model is synthesized from a training set of normal images that depict normal objects containing no abnormalities, and wherein the appearance model is synthesized using a texture model defining a texture distribution for the normal objects and a shape model defining a shape distribution for the normal objects; determining, by the processing system, at least one difference between the input image and the sample normal image; modifying, by the processing system, the sample normal image based at least in part on the at least one difference between the input image and the sample normal image; ceasing modification of the sample normal image based on a stopping criterion being met, wherein the stopping criteria is calculated based on a threshold decrease in the at least one difference between the input image and the sample normal image between consecutive iterations of the determining the at least one difference and the modifying the sample normal image; and identifying, by the processing system, an abnormality in the input image, wherein the abnormality is indicated by an area of the input image that does not conform to a corresponding area of the sample normal image.