USA As Represented by Secretary of the Navy

Jeffrey L Solka

Carey E Priebe

George W Rogers

Wendy L Poston

Harold H Szu

Richard A Lorey

Donald Vermillion

Erik G Julin

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.

System and method for incorporating segmentation boundaries into the

A self-organizing neural network and method for classifying a pattern signature having N-features is provided. The network provides a posteriori conditional class probability that the pattern signature belongs to a selected class from a plurality of classes with which the neural network was trained. In its training mode, a plurality of training vectors is processed to generate an N-feature, N-dimensional space defined by a set of non-overlapping trained clusters. Each training vector has N-feature coordinates and a class coordinate. Each trained cluster has a center and a radius defined by a vigilance parameter. The center of each trained cluster is a reference vector that represents a recursive mean of the N-feature coordinates from training vectors bounded by a corresponding trained cluster. Each reference vector defines a fractional probability associated with the selected class based upon a ratio of i) a count of training vectors from the selected class that are bounded by the corresponding trained cluster to ii) a total count of training vectors bounded by the corresponding trained cluster. In the exercise mode, an input vector defines the pattern signature to be classified. The input vector has N-feature coordinates associated with an unknown class. One of the reference vectors is selected so as to minimize differences with the N-feature coordinates of the input vector. The fractional probability of the selected one of the reference vectors is the a posteriori conditional class probability that the input vector belongs to the selected class.

Self-organizing neural network for classifying pattern signatures with

A discrete non-linear estimator is provided to establish a probability dety function useful in classification of a feature as belonging to one of two classes. A first and second known feature space associated with respective first and second classes are each converted to a plurality of voltages reflecting corresponding first and second feature space bins. A first and second one-dimensional, non-linear resistive grid has input nodes that continuously receive the voltages reflecting the respective first and second known feature space. Each resistive grid produces an output voltage at each of a corresponding plurality of first and second output nodes. Each of a plurality of voltage comparators are used to compare voltages at corresponding first and second output nodes to output a discrete probability density function that considers corresponding ones of the first and second feature space bins. A display may be used to visually or otherwise provide each probability density function from each voltage comparator.

Non-linear resistive grid kernel estimator useful in single feature,

A network is provided for the detection and correction of local boundary misalignments in a two-dimensional pixel space between a reference and transformed image. An input layer has input layer sections, each of which contains a plurality of input nodes associated with a cell. The cell is centered on a pixel and divided along a straightline orientation into first and second cell sections. Each of the input nodes outputs a digital signal indicative of the presence or absence of a contrast gradient as measured by the two cell sections. A second layer has a plurality of second layer sections, each of which is associated with one of the input layer sections and contains a plurality of second layer nodes. Each second layer node is responsive to a combination of input nodes to indicate the presence or absence of a boundary misalignment between the reference and transformed images. Presence of a contrast gradient at the combination of nodes defines a local boundary misalignment. A third layer has a plurality of third layer nodes, each of which is associated with one of the second layer sections. Each third layer node weights and combines outputs of the second layer nodes to output a signal defining a direction to shift the transformed image perpendicular to the straightline orientation. The third layer are outputs a signal defining the local correction of the local boundary misalignment between the reference and transformed images for the centered pixel.

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Fredericksburg, VA, United States of America

Jeffrey L Solka