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Learning from the Expert: Improving Boundary definitions in Biomedical Imagery

机译:从专家学习:改善生物医学图像中的边界定义

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Defining the boundaries of regions of interest in biomedical imagery has remained a difficult real-world problem in image processing. Experience with fully automated techniques has shown that it is usually quicker to manually delineate a boundary rather than correct the errors of the automation. Semi-automated, user-guided techniques such as Intelligent Scissors and Active Contour Models have proven more promising, since an expert guides the process. This paper will report and compare some recent results of another user-guided system, the Expert's Tracing Assistant, a system which learns a boundary definition from an expert, and then assists in the boundary tracing task. The learned boundary definition better reproduces expert behavior, since it does not rely on the a priori edge-definition assumptions of the other models.
机译:定义生物医学图像的兴趣区域的界限在图像处理中仍然是一个困难的真实问题。完全自动化技术的体验表明,手动描绘边界通常更快,而不是纠正自动化的错误。半自动,用户引导技术,如智能剪刀和主动轮廓模型,已被证明更有希望,因为专家指导该过程。本文将报告并比较另一个用户引导系统的最近结果,专家的追踪助手,一个系统从专家中学习边界定义,然后有助于边界跟踪任务。学习的边界定义更好地再现专家行为,因为它不依赖于其他模型的先验边缘定义假设。

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