基于高斯形状模型的变分水平集细胞分割。

A Gelas, K Mosaliganti, A Gouaillard, L Souhait, R Noche, N Obholzer, S G Megason
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引用次数: 0

摘要

在对基于显微镜的图像进行分析时,一个主要的挑战在于将由于过于密集而出现重叠的细胞分开。由于图像采集的物理特性导致像素强度的巨大变化,这项任务变得复杂。每张图像通常包含数千个细胞,每个细胞具有不同的方向、大小和强度直方图。在本文中,一个细胞核的空间强度模型被纳入[1],以帮助从显微镜数据集的细胞分割。定义了一个能量泛函,并利用它将原子核的空间强度分布建模为具有恒定强度背景的高斯分布。在多种微观数据上的实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VARIATIONAL LEVEL-SET WITH GAUSSIAN SHAPE MODEL FOR CELL SEGMENTATION.

In analysis of microscopy based images, a major challenge lies in splitting apart cells that appear to overlap because they are too densely packed. This task is complicated by the physics of the image acquisition that causes large variations in pixel intensities. Each image typically contains thousands of cells with each cell having a different orientation, size and intensity histogram. In this paper, a spatial intensity model of a nucleus is incorporated into [1] to aid cell segmentation from microscopy datasets. An energy functional is defined and with it the spatial intensity distribution of a nuclei is modeled as a Gaussian distribution with constant intensity background. Experimental results on a variety of microscopic data validate its effectiveness.

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