22 March 1996 Derivative-based feature saliency for computer-aided breast cancer detection and diagnosis
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Abstract
Derivative-based feature saliency techniques were used to define the best of 25 Laws texture features for the classification of 101 malignant mass and benign mass regions. Statistical and derivative-based saliency techniques were used to select the best size, shape, contrast, and Laws texture features for the mass model. Nine features were chosen to define the model, of which four have been used by other researchers. Using this model, the regions were classified using a multilayer perceptron neural network architecture trained with an imbalanced training set weight update algorithm to obtain an overall classification accuracy of 100 percent for the segmented malignant masses with a false-positive rates of 1.8/image. The system has shown a sensitivity of 92 percent for locating malignant ROIs. The database contained 284 images (12 bit, 100 micrometers ).
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William E. Polakowski, William E. Polakowski, Steven K. Rogers, Steven K. Rogers, Dennis W. Ruck, Dennis W. Ruck, Richard A. Raines, Richard A. Raines, Jeffrey W. Hoffmeister, Jeffrey W. Hoffmeister, } "Derivative-based feature saliency for computer-aided breast cancer detection and diagnosis", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235922; https://doi.org/10.1117/12.235922
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