Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1997-11-25
2001-09-04
Powell, Mark R. (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S015000, C706S016000
Reexamination Certificate
active
06285992
ABSTRACT:
FIELD OF THE INVENTION
This invention relates generally to detecting anomalies in complex data and, more particularly, to an artificial neural network based system useful in analyzing hierarchical data such as wavelet processed image data.
BACKGROUND OF THE INVENTION
Neural network based systems are known, however, known systems typically are not capable of analyzing a large collection of complex data. Rather, neural network based systems typically operate on subsets of the complex data. For example, and to detect abnormalities in image data, known neural network systems typically only analyze subsets of an entire data set, which reduces the effectiveness of the analysis.
One particular application in which this limitation of known neural network based systems has a significant adverse consequence is in mammography screening. Particularly, breast cancer is now estimated to strike one in eight adult American women, and many national institutions are promoting large-scale breast cancer screening programs. Even with the above described limitations, computer-aided diagnosis techniques have been applied to mammography screening programs and such techniques may offer substantial benefits in terms of cost reduction and increased effectiveness of the screening process. The use of computers to directly prescreen mammograms may eventually permit a substantial reduction in the number of studies that must be viewed by a radiologist. Of course, to achieve such a reduction, computers must be able to directly interpret digitized images and the process must be fully automated.
With respect to digitized images, mammograms show only an estimated 3% of their actual information content. Improvements in the visibility of mammographic information content will probably improve detection of small tumors. It is unlikely, however, that state-of-the-art screen-film radiography alone can be improved to display more information.
Wavelet transformation, a known image enhancement technique, has been used successfully to enhance the visibility of image information content in mammograms, including both masses and microcalcifications. Wavelet image representations also permit high magnitudes of data compression without loss of important image features.
To interpret the digitized images, several rule-based systems use thresholding, subtraction, or both. These techniques have been hampered by high false-positive detection rates. Artificial neural networks (ANN) are an alternative to traditional rule-based (symbolic) methods for computer-aided detection of mammographic lesions. ANNs learn the significance of image features based upon example training images. In general, ANNs are adept at pattern recognition problems.
As explained above, however, most all known ANN systems perform direct digitized data analysis of mammograms using only small regions of interest (ROI) selected from an entire image. Other ANN systems extract features, either qualitative or quantitative, for network training, or have incorporated ANNs into other computer-aided diagnosis schemes to improve lesion detection.
It would be desirable to provide a fully automated system for detecting anomalies in complex data, including for analyzing entire images to identify lesions. It also would be desirable to provide such a system which does not generate a high false-positive detection rate.
SUMMARY OF THE INVENTION
These and other objects may be attained by fully automated methods and systems for processing images to identify anomalies in which an entire set of hierarchical data (i.e., an ordered data set) is processed so that the entire set can be analyzed by a neural network. Such processing is performed utilizing wavelet processing. Particularly, and in one embodiment, multiresolution (five-level) and multidirection (two-dimensional) wavelet analysis with quadratic spline wavelets is performed to transform the data set. For example, and for image data, the wavelets are a first-order derivative of a smoothing function and enhance the edges of image objects. Because two-dimensional wavelet transformations quantize an image in terms of space and spatial frequency and can be ordered linearly, images can be processed recursively to determine prominent features.
In another aspect, the present invention relates to processing the preprocessed data so that the entire complex data set can be analyzed by a neural network. Particularly, the wavelet coefficients form a hierarchy which is linearized as a sequence of triplets: (coefficient, hierarchical level, position in level) by canonical topological sorting and parsed using a neural network approach based on sequential recursive auto-associative memory (SRAAM). Since the wavelet coefficients are continuous, linear output instead of sigmoidal output is used in the SRAAM. This variation is therefore referred to as linear output sequential recursive auto-associative memory, or LOSRAAM.
At the completion of LOSRAAM training, the coefficients and their context units so derived can be used in the network to predict the preceding wavelet coefficient triplet within a predetermined error tolerance. The activation pattern associated with the context units constitute a vector in state space. The input half of the LOSRAAM neural network maps the coefficient triplets and state space vectors into other state space vectors. The objective of LOSRAAM training is to make this mapping reversible using the output half of the LOSRAAM network. Thus, given any but the first wavelet coefficient information, the network can approximately predict the prior wavelet coefficient, its hierarchical level, its position within the level, and the activation of the context units that will predict the preceding wavelet coefficient triplet. Thus, the entire set of wavelet coefficients theoretically can be predicted from the last pair of hidden (context) units.
The LOSRAAM training process is repeated several times varying the number of context units and other training parameters, and each resultant LOSRAAM network may provide a different interpretation of the internal structure of the data. The set of context unit activation patterns arising from the LOSRAAM hidden layer defines a set of vectors in a state space. These vectors are subjected to cluster analysis.
The cluster analysis yields identifiable and discrete states. From these states, a feature vector is created for each image. Each element in the feature vector represents the number of times the corresponding state from the above cluster analysis is found in each image. The feature vectors are further processed from discrete counts to continuous, fuzzy values by weighting them according to the average fit each state vector achieves with the model state vector for that cluster. Thus, each image may be represented as a fuzzy feature vector (FFV).
Then, several feed forward neural networks (FFNNs) are trained to classify the FFVs. FFNNs differ according to their starting weights, the use of hint units, and other training parameters. Such FFNN training may be performed using the conjugate gradient method modified for use with a selfscaling error function, optimized to reduce the number of derivative computations, and monitored for restart conditions. The FFNN may also contain special (hint) output units whose target values are chosen to constrain training results by introducing coarse positional output information.
The above described system is fully automated for detecting anomalies in complex data, such as for prescreening images. For mammography studies, therefore, the system is capable of analyzing entire images to identify lesions. The system also does not generate a high false-positive detection rate.
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Kalman Barry L.
Kwasny Stanley C.
Reinus William R.
Armstrong Teasdale LLP
Powell Mark R.
Starks Wilbert
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