RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume

This paper proposes a rate-distortion (RD) based seeded region growing (SRG) for extracting an object such as breast tumors in ultrasound volumes which contain speckle noise and indistinct edges. In the proposed algorithm, region growing proceeds in such a way that the growing cost is minimized which is represented as the combination of rate measuring the roughness of a region contour and distortion measuring the inhomogeneity of pixels in a region. An input image is first segmented into an initial seed region and atomic homogeneous regions. The seed is next merged with one of adjacent regions which makes the RD cost minimum at each step. Such a merging is repeated until the RD cost averaged over the entire seed contour reaches the maximum. As a result, the final seed holds region homogeneity and has a smooth contour while maximizing inhomogeneity against its adjacent regions. Experiments of extracting breast tumors in four real ultrasound volumes show the proposed method yields the average 40% improvement in error rate with respect to the results extracted manually over some conventional methods.