Segmentation of cell nuclei from histological images by ellipse fitting

Jenni Hukkanen, A. Hategan, E. Sabo, I. Tabus
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引用次数: 19

Abstract

We propose a new algorithm for non-assisted segmentation of possibly clustered nuclei from histological images. We use elliptic shapes as parametric models to represent the nuclei contours and fit the parameters using the information present in the gray level intensity image and in the derived gradient image. Multiple seeds for each closed contour are found by ultimate erosion of an estimated edge image, resulting in an number of seeds generally larger than the number of nuclei. Our algorithm, called segmentation of nuclei by ellipse fitting (SNEF), constructs several candidate contours for each seed by fitting ellipses to selected subsets of edge pixels. In the end the algorithm selects the contours to be declared nuclei by comparing the values of a suitably chosen goodness of fit criterion. The proposed algorithm produces segmentations in agreement with an expert pathologist.
基于椭圆拟合的组织图像细胞核分割
我们提出了一种新的算法,用于从组织学图像中非辅助分割可能聚集的细胞核。我们使用椭圆形状作为参数模型来表示核轮廓,并使用灰度强度图像和派生的梯度图像中存在的信息来拟合参数。通过对估计边缘图像的最终侵蚀,每个封闭轮廓都有多个种子,导致种子的数量通常大于核的数量。我们的算法被称为椭圆拟合核分割(SNEF),通过将椭圆拟合到选定的边缘像素子集,为每个种子构建几个候选轮廓。最后,该算法通过比较合适的拟合优度准则的值来选择要声明核的轮廓。该算法产生的分割与专家病理学家一致。
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