US 5,671,294 AGranted

System and method for incorporating segmentation boundaries into the calculation of fractal dimension features for texture discrimination

AssigneeThe United States of America as represented by the Secretary of the Navy

InventorsGeorge W. Rogers, Carey E. Priebe, Jeffrey L. Solka, Richard A. Lorey, Erik G. Julin

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Abstract

Image analysis is performed by defining segmentation boundaries within an image by using wavelet theory or some other suitable method. Such boundaries can be incomplete, irregular, and/or multi-valued. The segmentation boundaries are then incorporated into feature calculations related to fractal dimensions for each pixel using a diffusion related method or a Dijkstra potential related method. These features are then used in statistical techniques to distinguish among textures or classes of interest. The system performing the image analysis is trained (or supervised) on data from different classes within an image or images. This enables the system to then later identify these classes in different images. The system can be used for Computer Aided Diagnosis (CAD) of mammograms or other medical imagery.

Claims15

Dependent claims

A method for distinguishing and classifying textures of an image in a supervised feature generation system, said method comprising the steps of:

generating a digitized image representative of said image;
reading and storing a digitized image file in dependence upon said digitized image;
reading and storing texture classification information representative of selected pixels within said image;
reading and storing a segmentation map representative of segmentation boundaries within said image;
recursively computing bounding areas within said image as a function of scale in dependence upon said digitized image file and said segmentation map;
computing intensity differences among bounding areas for each scale;
determining local area averages for each said scale in dependence upon said intensity differences and said segmentation map;
computing power law features;
merging said power law features and said texture classification information to produce a file of distinct and labeled features;
building a probability density function of said image using adaptive mixtures, by estimating a probability density function for each class of texture within said image and then determining class discriminant boundaries in dependence upon a maximum likelihood ratio;
and,
determining texture classes within a new digital image by comparing the probability density function of each pixel within said new digital image to previously determined discriminant boundaries and then labeling said texture classes within said new digital image with a probabilistic ranking based on probability density functions previously determined from known texture classes.

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