A novel 2D wavelet level energy for breast lesion classification on ultrasound images

Infiltrative nature is a unique characteristic of breast cancer. Cross-sectional view of infiltrative nature can find a rough lesion contour on ultrasound image. Roughness description is crucial for clinical diagnosis of breast lesions. Based on boundary tracking, traditional roughness descriptors usually suffer from information loss due to dimension reduction. In this paper, a novel 2-D wavelet-based energy feature is proposed for breast lesion classification on ultrasound images. This approach characterizes the roughness of breast lesion contour with normalized spatial frequency components. Feature efficacies are evaluated by using two breast sonogram datasets with lesion contour delineated by an experienced physician and the ImageJ, respectively. Experimental results show that the new feature can obtain excellent performance and robust contour variation resistance.