SHAPE-BASED INTERPOLATION OF TREE-LIKE STRUCTURES IN 3-DIMENSIONAL IMAGES
SHAPE-BASED INTERPOLATION OF TREE-LIKE STRUCTURES IN 3-DIMENSIONAL IMAGES
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DOI：
10.1109/42.241871
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发表时间：
1993-09-01
影响因子：
10.6
通讯作者：
RITMAN, EL
中科院分区：
文献类型：
--
作者：
HIGGINS, WE;MORICE, C;RITMAN, EL
可从许多医学成像扫描仪获得的三维（3-D）图像通常在z方向上具有比在x或y方向上更低的分辨率。在提取和显示这些图像中的对象之前，通常通过诸如线性插值的技术生成插值的3-D灰度图像以填充“缺失的切片”。不幸的是，线性插值和相关方案产生的三维图像具有模糊的物体结构。因此，当从内插图像中提取并显示对象时，对象通常呈现出块状的且通常不令人满意的外观。这个问题对于诸如冠状动脉之类的细树状结构尤其严重。最近，该领域的工作人员提出了一种称为基于形状的插值的策略，该策略提供了对线性插值的改进。在基于形状的插值中，首先从初始3-D图像分割（提取）感兴趣的对象以产生低z分辨率二进制值图像。然后，对分割后的图像进行插值，得到高分辨率的二值三维图像。然而，这些技术不使用原始灰度信息，并且难以处理包含树状结构的图像，例如冠状动脉。我们描述了两种基于形状的插值方法，产生改进的结果，树状结构。第一种方法结合了几何约束，并作为输入的原始3-D图像的分割版本。第二种方法建立在第一种方法的基础上，因为它也使用原始灰度图像作为第二输入。冠状动脉树的3-D图像的测试证明了该方法的有效性。
Three-dimensional (3-D) images obtainable from many medical-imaging scanners typically have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the ''missing slices.'' Unfortunately, linear interpolation and related schemes produce a 3-D image having blurred object structures. Thus, when objects are extracted and displayed from the interpolated image, the objects often exhibit a blocky and generally unsatisfactory appearance. This problem is particularly acute for thin tree-like structures such as the coronary arteries. Recently, workers in the field have proposed a strategy referred to as shape-based interpolation that offers an improvement to linear interpolation. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image. Then, the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. These techniques, however, do not use the original gray-scale information and have difficulties with images containing tree-like structures, such as the coronary arteries. We describe two shape-based interpolation methods that generate improved results for tree-like structures. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds upon the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.