ULTRASOUND TISSUE CHARACTERIZATION OF BREAST BIOPSY SPECIMENS - EXPANDED STUDY

Tissue classification by examining sets of ultrasound parameters is an elusive goal. We report analysis of measurements of ultrasound speed, attenuation and backscatter in the range 3 to 8 MHz in breast tissues at 37 C. Statistical discriminant analysis and neural net analysis we re employed. Data were acquired from 24 biopsy and 7 mastectomy specim ens. Best separation of the classes normal, benign, and malignant occu rred in the 18 cases where two tissue classes were present in the same specimen and parameters were corrected for within-patient mean; then 85-90% of cases in test sets were correctly classified. Most errors co mprised misclassified benign cases. The neural net was comparable to d iscriminant analysis and slightly superior in separating normal and ma lignant classes.