Database-guided breast tumor detection and segmentation in 2D ultrasound images
Jingdan Zhang, Shaohua Kevin Zhou, Shelby S. Brunke, Carol Lowery, Dorin Comaniciu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Ultrasonography is a valuable technique for diagnosing breast cancer. Computer-aided tumor detection and segmentation in ultrasound images can reduce labor cost and streamline clinic workflows. In this paper, we propose a fully automatic system to detect and segment breast tumors in 2D ultrasound images. Our system, based on database-guided techniques, learns the knowledge of breast tumor appearance exemplified by expert annotations. For tumor detection, we train a classifier to discriminate between tumors and their background. For tumor segmentation, we propose a discriminative graph cut approach, where both the data fidelity and compatibility functions are learned discriminatively. The performance of the proposed algorithms is demonstrated on a large set of 347 images, achieving a mean contour-to-contour error of 3.75 pixels with about 4.33 seconds.