Wu Hai-Shan, Murray Jacinta, Morgello Susan
Department of Pathology, Box 1194, Mount Sinai School of Medicine, One Gustave L. Levy Place, New York, New York 10029, E-mail:
J Imaging Sci Technol. 2008;52(4):405021-4050211. doi: 10.2352/J.ImagingSci.Technol.(2008)52:4(040502).
A generalized clustering algorithm utilizing the geometrical shapes of clusters for segmentation of colored brain immunohistological images is presented. To simplify the computation, the dimension of vectors composed from the pixel RGB components is reduced from three to two by applying a de-correlation mapping with the orthogonal bases of the eigenvectors of the auto-covariance matrix. Since the brain immunohistochemical images have stretched clusters that appear long and narrow in geometrical shape, we use centroids of straight lines instead of single points to approximate the clusters. An iterative algorithm is developed to optimize the linear centroids by minimizing the approximation mean-squared error. The partitioning of the two-dimensional vector domain into three portions classifies each image pixel into one of the three classes: The microglial cell cytoplasm, the combined hematoxylin stained cell nuclei and the neuropil, and the pale background. Regions of the combined hematoxylin stained cell nuclei and the neuropil are to be separated based on the differences in their regional shapes. The segmentation results of real immunohistochemical images of brain microglia are provided and discussed.
提出了一种利用聚类几何形状对彩色脑免疫组织学图像进行分割的广义聚类算法。为简化计算，通过应用与自协方差矩阵特征向量的正交基的去相关映射，将由像素RGB分量组成的向量维度从三维降至二维。由于脑免疫组化图像中的聚类呈细长的几何形状，我们使用直线的质心而非单个点来近似聚类。开发了一种迭代算法，通过最小化近似均方误差来优化线性质心。将二维向量域划分为三个部分，可将每个图像像素分类为三类之一：小胶质细胞胞质、苏木精染色的细胞核与神经纤维的组合以及浅色背景。基于苏木精染色的细胞核与神经纤维组合区域在形状上的差异对其进行分离。给出并讨论了脑小胶质细胞真实免疫组化图像的分割结果。
