Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections
Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections
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DOI：
10.1111/j.0022-2720.2004.01338.x
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发表时间：
2004-07-01
影响因子：
2
通讯作者：
Bengtsson, E
中科院分区：
文献类型：
--
作者：
Wählby, C;Sintorn, IM;Bengtsson, E
我们提出了一种基于区域的分割方法，该方法通过结合原始图像的形态滤波和图像的梯度幅度来创建代表目标和背景像素的种子。然后将种子用作梯度幅度图像的分水岭分割的起点。以慷慨的方式完成全自动播种，从而在每个前景对象中设置至少一个种子。如果在单个对象中放置多个种子，分水岭分割将导致初始过分割，即在没有强边缘的地方创建边界。因此，初始分割的结果通过基于沿分隔相邻对象的边界的梯度大小的合并来进一步细化。此步骤还可以轻松删除对比度较差的对象。作为最后一步，分离出成团的原子核。根据星团的形状。完整分割过程的输入参数数量只有五个。这些参数可以使用测试图像来手动设置，并且此后可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，验证了该自动化系统的有效性。对二维和三维图像的分割准确率都达到了90%左右。
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.