Wählby C, Sintorn I-M, Erlandsson F, Borgefors G, Bengtsson E
Centre for Image Analysis, Uppsala University, Sweden.
J Microsc. 2004 Jul;215(Pt 1):67-76. doi: 10.1111/j.0022-2720.2004.01338.x.
We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.
我们提出了一种基于区域的分割方法，其中通过对原始图像及其梯度幅值进行形态学滤波相结合来创建代表目标像素和背景像素的种子。然后将这些种子用作梯度幅值图像分水岭分割的起始点。全自动种子生成以一种宽松的方式进行，以便在每个前景目标中至少设置一个种子。如果在单个目标中放置了多个种子，分水岭分割将导致初始过度分割，即在没有强边缘的地方创建边界。因此，通过基于沿分隔相邻目标的边界的梯度幅值进行合并，对初始分割结果进行进一步细化。这一步骤也使得去除对比度差的目标变得容易。作为最后一步，根据细胞核簇的形状将其分离。完整分割过程的输入参数数量仅为五个。这些参数可以使用测试图像手动设置，然后用于在类似成像条件下创建的大量图像。通过与来自相同图像区域的手动计数进行比较，验证了这个自动化系统。二维和三维图像均实现了约90%的正确分割。
