3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed
3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed
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DOI：
10.3390/jimaging2040031
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发表时间：
2016-12-01
影响因子：
3.2
通讯作者：
Sousa-Neves, Rui
中科院分区：
文献类型：
--
作者：
Atta-Fosu, Thomas;Guo, Weihong;Sousa-Neves, Rui
图像分割是将目标从背景中分离出来的一个重要过程。应用于细胞，结果可用于细胞计数，这在医学诊断和治疗以及科学家和医学从业者经常使用的生物研究中非常重要。分割包含不同形状和大小的细胞的3D共聚焦显微镜图像仍然具有挑战性，因为细胞核是紧密堆积的。分水岭变换提供了一个有效的工具，在分割这样的核提供了一个合理的一组标记，可以在图像中找到。当给定图像中存在低对比度变化或过多噪声时，分水岭变换会导致过度分割（单个对象被过度分割为多个对象）。传统的分水岭使用输入图像的局部最小值，并且通常会在一个对象中找到多个最小值，除非它们被指定（标记控制分水岭）。使用局部最小值的一种替代方法是通过称为种子分水岭的监督技术，该技术提供单个种子来替换对象的最小值。因此，种子分水岭算法的精度依赖于预定义种子的精度。在本文中，我们提出了一种分割方法的基础上的几何形态学特性的“景观”使用曲率。曲率被计算为形状矩阵的特征值，从而产生精确的种子，这些种子也继承其各自单元的原始形状。我们比较了一些流行的方法，并显示所提出的方法的优势。
Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the 'landscape' using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method.