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An Improved Segmentation of Chromosomes in Q-Band Prometaphase Images Using a Region Based Level Set

机译:使用基于区域的水平集改进的Q波段前中期图像中染色体的分割

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Karyotype analysis is a widespread procedure in cytogenetics to assess the possible presence of genetics defects. The procedure is lengthy and repetitive, so that an automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and full disentangling of chromosomes are open issues. The first step in every automatic procedure, is the segmentation of the chromosomes, as either single entities or in clusters, in the image. The better the segmentation step, the easier the subsequent disentanglement. We propose for the segmentation step a region based level set algorithm that is able to address the variability in the image background due to the presence of hyper- or hypo-fluorescent regions in the image. We compare its performance with other algorithms proposed in the literature for the segmentation of chromosomes, over a set of 11 manually annotated images. We show the superiority of the proposed approach both in terms of pixel sensitivity, and in terms of number of separate clusters with respect to the manual segmentation. The images used in the paper are available for public download.
机译:核型分析是在细胞遗传学中评估遗传缺陷可能存在的广泛方法。该过程是冗长且重复的,因此自动分析将大大有助于细胞遗传学家的常规工作。尽管如此,染色体的自动分割和完全解开仍是未解决的问题。在每个自动程序中的第一步是对图像中的单个实体或成簇的染色体进行分割。分割步骤越好,随后的纠缠就越容易。我们为分割步骤提出了一种基于区域的水平集算法,该算法能够解决图像背景中由于图像中存在高荧光区域或低荧光区域而引起的变化。我们将其性能与文献中提出的其他算法进行了比较,该算法在一组11个手动注释的图像上进行了染色体分割。我们在像素灵敏度方面以及相对于手动分割的单独群集数量方面都显示了所提出方法的优越性。本文中使用的图像可供公众下载。

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