首页> 外文会议>IEEE International Conference on Image Processing;ICIP 2012 >Segmentation of neural stem cells/neurospheres in high content brightfield microscopy images using localized level sets
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Segmentation of neural stem cells/neurospheres in high content brightfield microscopy images using localized level sets

机译:使用局部水平集对高内涵明场显微镜图像中的神经干细胞/神经球进行分割

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Neural stem cells and neural progenitors are early nervous system cells that form neurospheres when propagated in vitro. We study changes in growth using brightfield images to understand the effects of drugs. The image quality is generally poor, imposing challenges for automatic analysis. Level-set segmentation methods are able to handle topology changes but require close initializations for accurate and efficient results. Global level-set methods using single image-wide optimization objective functions are difficult to cope with large illumination and shading changes. We propose to adopt Hough transform to initialize localized level-sets for cell segmentation. Experimental results on 480 images with 738 neurospheres show that our proposed method performs best over existing level-set methods without appropriate initial contours.
机译:神经干细胞和神经祖细胞是早期神经系统细胞,在体外繁殖时会形成神经球。我们使用明场图像研究生长的变化,以了解药物的作用。图像质量通常很差,给自动分析带来了挑战。水平集分段方法能够处理拓扑更改,但需要进行紧密的初始化才能获得准确而有效的结果。使用单个图像范围优化目标函数的全局水平设置方法很难应对较大的照明和阴影变化。我们建议采用霍夫变换来初始化用于细胞分割的局部化水平集。在具有738个神经球的480张图像上的实验结果表明,我们提出的方法在没有适当初始轮廓的情况下优于现有的水平集方法。

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