Robust cell nuclei segmentation using statistical modelling

T. Mouroutis, S. Roberts, A. Bharath
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引用次数: 106

Abstract

The objective analysis of cytological and histological images has been the subject of research for many years. One of the most difficult fields in histological image analysis is the automated segmentation of tissue-section images. We propose a multistage segmentation method for the isolation of cell nuclei in such images. In the first stage the compact Hough transform (CHT) is used to determine possible locations of the nuclei. We then define a likelihood function which enables us to perform an optimization procedure based on the maximization of this function. Global grey-level histogram information is used thoughout the algorithm to reduce the amount of computation and to reject unwanted artefacts. The algorithm is tested on connective tissue images with very encouraging results. Apart from detecting well-separated nuclei in the images, it performs well in separating dividing nuclei into likely substructures. At the same time the algorithm provides us with a measure of uncertainty along the detected boundary, in the form of the value of the likelihood function.
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稳健细胞核分割使用统计建模
细胞学和组织学图像的客观分析是多年来研究的课题。组织切片图像的自动分割是组织图像分析中最困难的领域之一。我们提出了一种多阶段分割方法分离细胞核在这样的图像。在第一阶段,紧凑霍夫变换(CHT)被用来确定原子核的可能位置。然后,我们定义一个似然函数,使我们能够基于该函数的最大化执行优化过程。在整个算法中使用全局灰度直方图信息来减少计算量并拒绝不需要的伪影。该算法在结缔组织图像上进行了测试,取得了令人鼓舞的结果。除了在图像中检测分离良好的原子核外,它还能很好地将分裂的原子核分离成可能的亚结构。同时,该算法以似然函数的值的形式为我们提供了沿检测边界的不确定性度量。
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