3D segmentation of breast tumor in ultrasound images

Abstract

This paper proposes a three-dimensional (3D) region-based segmentation algorithm for extracting a diagnostic tumor from ultrasound images by using a split-and-merge and seeded region growing with a distortion-based homogeneity cost. In the proposed algorithm, 2D cutting planes are first obtained by the equiangular revolution of a cross sectional plane on a reference axis for a 3D volume data. In each cutting plane, an elliptic seed mask that is included tightly in a tumor of interest is set. At the same time, each plane is finely segmented using the split-and-merge with a distortion-based cost. In the result segmented finely, all of the regions that are across or contained in the elliptic seed mask are then merged. The merged region is taken as a seed region for the seeded region growing. In the seeded region growing, the seed region is recursively merged with adjacent regions until a predefined condition is reached. Then, the contour of the final seed region is extracted as a contour of the tumor. Finally, a 3D volume of the tumor is rendered from the set of tumor contours obtained for the entire cutting planes. Experimental results for a 3D artificial volume data show that the proposed method yields maximum three times reduction in error rate over the Krivanek’s method. For a real 3D ultrasonic volume data, the error rates of the proposed method are shown to be lower than 17% when the results obtained manually are used as a reference data. It also is found that the contours of the tumor extracted by the proposed algorithm coincide closely with those estimated by human vision.