CSIRO Mathematics, Informatics and Statistics Division, Locked Bag 17, North Ryde, New South Wales 2113, Australia.
J Microsc. 2013 Jul;251(1):57-67. doi: 10.1111/jmi.12043. Epub 2013 May 20.
Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.
从组织切片图像中分割细胞核对于许多生物学和生物医学研究都非常重要。许多现有的图像分割算法可能会导致聚类细胞核图像的过度分割或欠分割。在本文中，我们提出了一种新的基于凹曲线扩展的图像分割算法，用于从原始图像中正确、准确地提取标记。然后使用标记控制分水岭对聚类细胞核进行分割。该算法在合成图像和真实图像上进行了测试，与一些其他最先进的方法相比，取得了更好的结果。
