Breast Tissue Classification Using Diagnostic Ultrasound and Pattern Recognition Techniques: II. Experimental Results

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Abstract
The methods of statistical pattern recognition have been applied to the problem of in vivo ultrasonic characterization of breast disease in humans. Backscattered A-mode signals obtained from a commercial pulse imaging system were used to generate a large set of potentially useful features. Using statistical tests, a small subset of discriminatory features was selected to design a Bayes decision rule for each of two tissue classification schemes: malignant disease vs. benign disease, and malignant disease vs. (benign disease + normal tissue). Classification results obtained by the rotation method included sensitivities of 88 percent and 76 percent for the two schemes, based on data obtained from 32 women. These results are encouraging, though a definitive statement concerning the extrapolation of these numbers to the general population should only be made after obtaining results with a large data base.