Department of Informatics, University of Thessaloniki, Thessaloniki 540 06, Greece.
IEEE Trans Image Process. 1999;8(12):1744-56. doi: 10.1109/83.806620.
We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.
我们提出了一种基于模式分类的方法，用于在图像体积中进行三维（3-D）物体建模和分割。3-D 物体被描述为一组重叠的椭球体。分割依赖于几何模型和灰度统计。椭球体和灰度统计的特征参数被嵌入到径向基函数（RBF）网络中，并通过无监督训练来找到。在这项研究中，我们采用了一种基于α修剪均值统计的新的 RBF 网络鲁棒训练算法。通过使用球坐标系，将 Hough 变换算法扩展到 3-D 空间中，用于椭球中心估计。我们研究了所提出算法的性能，并展示了在对一组显微镜图像进行分割时的结果。
