首页> 外文会议>International symposium on advances in visual computing;ISVC 2009 >Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening
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Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening

机译:黎曼扩张曲线闭合:在宫颈癌自动筛查中的细分应用

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In this paper, we describe a nuclei segmentation algorithm for Pap smears that uses anisotropic dilation for curve closing. Edge detection methods often return broken edges that need to be closed to achieve a proper segmentation. Our method performs dilation using Riemannian distance maps that are derived from the local structure tensor field in the image. We show that our curve closing improve the segmentation along weak edges and significantly increases the overall performance of segmentation. This is validated in a thorough study on realistic synthetic cell images from our Pap smear simulator. The algorithm is also demonstrated on bright-field microscope images of real Pap smears from cervical cancer screening.
机译:在本文中,我们描述了针对子宫颈抹片涂片的核分割算法,该算法使用各向异性膨胀进行曲线闭合。边缘检测方法通常会返回需要闭合以实现正确分割的折断边缘。我们的方法使用从图像中的局部结构张量场得出的黎曼距离图执行膨胀。我们表明,曲线闭合改善了沿弱边缘的分割,并显着提高了分割的整体性能。这在我们的子宫颈抹片检查模拟器对真实的合成细胞图像进行的全面研究中得到了验证。该算法还在子宫颈癌筛查的真正子宫颈抹片检查的明场显微镜图像上得到了证明。

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