Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
412045 | Neurocomputing | 2015 | 13 Pages |
The analysis of 3D medical images becomes necessary since the 3D imaging techniques have been more and more widely applied in medical applications. This paper introduces a novel segmentation method for extracting objects of interest (OOI) in 3D ultrasound images. In the proposed method, a bilateral filtering model is first applied to a 3D ultrasound volume data set for speckle reduction. We then take advantage of graph theory to construct a 3D graph, and merge sub-graphs into larger one during the segmentation process. Therefore, the proposed method can be called a 3D graph-based segmentation algorithm. After the mergence of sub-graphs, a set of minimum spanning trees each of which corresponds to a 3D sub-region is generated. In terms of segmentation accuracy, the experiments using an ultrasound fetus phantom, a resolution phantom and human fingers demonstrate that the proposed method outperforms the 3D Snake and Fuzzy C means clustering methods, indicating improved performance for potential clinical applications.