基于矢量量化的三维图像自适应分割方法及其应用

A Vector Quantization Based Adaptive Three dimensional Image Segmentation Method and Its Applications

近年来对图像处理的研究已从二维(2D)向三维(3D)及更高维方向发展。但对3D图像分割的研究目前尚不够深入,仍以基于2D图像的分割方法为主。提出了一种可有效利用空间信息的3D图像自适应分割方法:先进行层间插值、空间子块的边缘与非边缘模式分类;并对非边缘模式子块进行基于矢量量化的分割,同时设计出一种最优码本求取方法来自适应确定分割的数目;再对边缘模式子块根据非边缘模式子块的分割结果进行逐点检测和划分。文中利用IBSR医学图像库的仿真人脑数据和实际人脑核磁共振成像(MRI)样本进行实验,验证了该方法的有效性。同时通过对同一病患不同时期的MRI数据样本进行实验,得到了诸如不同时间病灶部位的体积变化情况等十分有价值的临床医学信息。

In recent years, the research of image processing is developed from traditional two dimensions （2D） to three dimensions （3D） or even more dimensions. However, the existing segmentation methods are mainly based on 2D image processing, and more effective 3D image segmentation methods are expected. An adaptive 3D image segmentation method based on vector quantization （VQ） that can effectively utilize the spatial information of the volume data of the 3D image is proposed. In the method, a preprocessing is conducted on the 3D image, including volume interpolation, dividing of the 3D image into small sub-cubic blocks （sub-cubes）, and classification of the sub-cubes into two patterns, the edge pattern and non-edge pattern. The non-edge pattern sub-cubes are segmented by using the VQ technique and the edge pattern sub-cubes are classified in pixel based on the segmentation results of non-edge pattern sub-cubes. In order to determine the segmentation number adaptively, an optimal codebook searching algorithm is designed for the VQ approach. Experiments are conducted by using both the simulation samples and real human brain magnetic resonance imaging （MRI） images from the IBSR database and the effectiveness of the proposed method is validated by the experimental results. The experiments are also performed on the MRI images of the same patient in different treatment periods, which can provide the varied 3D information about the focus parts in different times that is valuable for clinical diagnosis in medical practice.