P6C-6 Breast Ultrasound Computer-Aided Diagnosis Using Both Acoustic and Image Features

Abstract

Acoustic, texture, and morphological features of the breast in ultrasound imaging were extracted for computer-aided diagnosis. In addition, correlations among different categories of features were analyzed. Clinical data from 14 patients (7 malignant and 7 benign samples) were acquired. A custom-made setup was used for simultaneous data acquisition of B-mode ultrasound and limited-angle tomography images. Texture features were extracted from B-mode images, including five parameters derived from the gray-level concurrence matrix and five parameters derived from a nonseparable wavelet transform. Morphological features were also extracted from B-mode images, including the depth-to-width ratio and normalized radial gradient. Acoustic features were estimated using limited-angle tomography, including the sound velocity and attenuation coefficient. Generally, the correlation coefficients for features within the texture feature group were relatively high (0.48-0.79), whereas those between different feature categories were relatively low (0.17-0.40). This suggests that combining different sets of features would improve the computer-aided diagnosis of breast cancer.