Three-Dimensional Region-Based Segmentation for Breast Tumors on Sonography

Abstract

Because malignant and benign breast tumors show different shapes and sizes on sonography, information about tumor shapes and sizes is important for clinical diagnosis. Since sonograms include noise and tissue texture, accurate clinical diagnosis is highly dependent on clinical experience and expertise. However, manually sketching a 3-dimensional (3D) breast tumor contour is a time-consuming and complicated task. Automatic contouring, which provides a contour similar to that of manual sketching of a breast tumor on sonography, may improve diagnostic accuracy. This study presents an efficient method for automatically detecting 3D contours of breast tumors on 3D sonography. The proposed method applies a voxel nearest neighbor filter, a Wiener filter, and an unsharp filter to enhance contrast and reduce noise. After a 3D region-growing algorithm is used to obtain the contour of the breast tumor, postprocessing of the extracted contour is performed to diminish the shadow region of the tumor. This study evaluated 20 tumor cases comprising 10 benign and 10 malignant cases. The results of computer simulation reveal that the proposed 3D segmentation method provides robust contouring for breast sonograms. This approach consistently obtains contours similar to those obtained by manual contouring of a breast tumor and can reduce the time needed to sketch precise contours.