Shadow effects in ultrasonic breast cancer imaging

In order to improve the ability of ultrasound (US) in the classification of benign and malignant breast tumors, a survey of shadow effects in B-scan imaging is presented in this paper. In the preliminary work, a set of agar base tissue mimicking phantoms was fabricated with certain regions imitating different levels of backscattering parts. Texture analysis and the proposed feature i.e. brightness of shadow were used to classify phantom composition. Comparison of back-propagation (BP) training errors was made between the classifier with and without consideration of shadow effects. Classification was implemented only on four digitized sonograms due to the lack of real images. Experimental results show that by taking into consideration the shadow effects, the neural networks show a higher rate of convergence and a smaller training error. These findings suggest that shadow effects can be computerized as an additional feature for classification of breast lesions.