Core atoms and the spectra of scale
Core atoms and the spectra of scale
复制标题
核心原子和尺度光谱
DOI：
10.1117/12.274150
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发表时间：
1997
影响因子：
4.6
通讯作者：
S. Pizer
中科院分区：
文献类型：
--
作者：
G. Stetten;R. Landesman;S. Pizer
我们的目的是表征数字在医学图像中作为第一步寻找和测量解剖结构。对于临床应用，我们需要完全自动化和合理的短计算时间。我们不要求确定一个清晰的边界，只要求识别结构并测量其大小和形状。我们的方法涉及到检测和链接的图像内的位置，具有高的“medialness”，即位置，从两个相对的边界等距。该方法产生核心原子的群体，每个核心原子由中心点和两个相关的边界点组成。我们可以通过原子中心的接近度和大小的相似性来将核心原子聚集在一起。我们生成聚类的统计签名来识别潜在的数字。特别是，我们计算了团簇的三个光谱与尺度的关系，包括（1）幅度：核心原子的数量，（2）偏心率：它们的聚集方向不对称性，以及（3）取向：它们的聚集方向。我们说明了这些光谱的各种图形测试图像的生产，展示平移，旋转和规模的光谱不变性，以及目标之间的特异性。我们观察图像噪声对光谱的影响，并展示了聚类如何减少这些影响。早期的研究结果表明，核心原子的标度光谱提供了一种有效和鲁棒的方法来识别图形，适合于在医学图像分析中的实际应用。
Our purpose is to characterize figures in medical images as a first step toward finding and measuring anatomical structures. FOr clinical use, we require complete automation and reasonably short computation times. We do not require that a sharp boundary be determined, only that the structure be identified and measurements taken of its size and shape. Our method involves the detection and linking of locations within an image that possess high 'medialness', i.e. locations that are equidistant from two opposing boundaries. The method produces populations of core atoms, each core atom consisting of a center point and the two associated boundary points. We can cluster core atoms by the proximity of their centers and by the similarity of their size. We generate statistical signatures of clusters to identify the underlying figure. In particular, we compute three spectra vs. scale for a cluster, including (1) magnitude: the number of core atoms, (2) eccentricity: their aggregate directional asymmetry, and (3) orientation: their aggregate direction. We illustrate the production of these spectra for various graphical test images, demonstrating translational, rotational, and scale invariance of the spectra, as well as specificity between targets. We observe the effects of image noise on the spectra and show how clustering reduces these effects. Early results suggest that the scale spectra of core atoms provide an efficient and robust method for identifying figures, suitable for practical application in medical image analysis.