A robust algorithm based on nonstationary degree for ultrasonic data enhancement

Compared with other medical imaging modalities, ultrasound imaging has its own advantages. The three-dimensional (3-D) ultrasound stereo visualization technique has a broad promising future for its ability superior to traditional two-dimensional (2-D) ultrasound image, and it helps to understand the complex structures of tissues as well as to measure tissular volumes. However, it is often difficult to interpret the 3-D structure from acoustic data because of the speckle noise. To solve this problem, we propose a new enhancement algorithm which is based on calculating nonstationary degree of ultrasound data to improve the image quality. We observe data within a finite length window and then map them to an N-dimensional space where every point represents the observed data. According to the data features, we divide this space into two parts: the stationary subspace constituted by the stationary points, which represent a line in the space, and the nonstationary subspace is formed by the points out of the line. Then the nonstationary degree of a set of observed data is defined as the distance from the correspondent point to the stationary line. Thus, we can enhance the image since the nonstationary degree is larger on tissular border where features vary rapidly. Finally, the application of the proposed algorithm to real data of the liver of a rabbit is described. The results are shown by means of 3-D ultrasound stereo visualization, and the results demonstrate a significant improvement compared with the original image.