Texture analysis of breast tumors on sonograms.

We performed a feasibility study to determine if the texture features extracted from sonograms can be used to predict malignant or benign breast pathology by the proposed artificial neural network and to compare the diagnostic results with the radiologists' results. A total of 1,020 images (4 different rectangular regions from the 2 orthogonal imaging planes of each tumor) from 255 patients were used as samples. When a sonogram was performed, 1 physician identified the region of interest in the sonogram; then, a neural network model, using 24 autocorrelation texture features, classified the tumor as benign or malignant. Three radiologists who were unfamiliar with the samples also classified these images. The receiver operating characteristic (ROC) area index for the proposed neural network system is 0.9840 +/- 0.0072. The neural network identified 35 of 36 malignancies and 211 of 219 benign tumors using all 4 regions of interest. The radiologists, on average, identified 19 of 36 malignancies, with 12 tumors called indeterminate and 4 tumors called benign. We conclude that benign and malignant breast tumors can be distinguished using interpixel correlation in digital ultrasonic images.