ABSTRACT
Development of multi-detector row computed tomography (MDCT) has enabled three-dimensions (3D) scanning with minute voxels. Minute voxels improve spatial resolution of CT images. At the same time, however, they increase image noise. Multi-planar reconstruction (MPR) is one of effective 3D-image processing techniques. The conventional MPR technique can adjust slice thickness of MPR images. When a thick slice is used, the image noise is decreased. In this case, however, spatial resolution is deteriorated. In order to deal with this trade-off problem, we have developed the weighted-averaging multi-planar reconstruction (W-MPR) technique to control the balance between the spatial resolution and noise. The weighted-average is determined by the Gaussian-type weighting function. In this study, we compared the performance of W-MPR with that of conventional simple-addition-averaging MPR. As a result, we could confirm that W-MPR can decrease the image noise without significant deterioration of spatial resolution. W-MPR can adjust freely the weight for each slice by changing the shape of the weighting function. Therefore, W-MPR can allow us to select a proper balance of spatial resolution and noise and at the same time produce suitable MPR images for observation of targeted anatomical structures.
Subject(s)
Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Multidetector Computed Tomography/methods , Phantoms, ImagingABSTRACT
CT is an effective tool for image diagnosis because it enables noninvasive observation of internal organs. In the course of CT, 3D-CT, such as a helical scanning CT and a multi-detector row CT, has been developed. With the use of 3D-CT, organs can be observed from several viewing directions. Even now, however, 3D-CT images are generated by manual procedures to extract objective organs using the threshold method. These procedures waste time and effort. Therefore, development of highly automated and effective extracting techniques has been desired. The region growing (RG) method is one of the effective techniques of extracting internal organs. The conventional RG method, however, has some defects. Extracted regions are strongly affected by the threshold value for segmentation. A break point on a region contour yields a leakage of region. To overcome these defects, we formulated a modified region growing method with edge detection (MRGWED) which combines a three-dimensional region growing technique and an edge detection technique. Using the MRGWED, we tried to extract teeth from dentomaxillofacial 3D-CT image data.
