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Learning from the Expert: Improving Boundary Definitions in Biomedical Imagery

机译:向专家学习:改善生物医学图像的边界定义

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摘要

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:在图像处理中,定义生物医学图像中感兴趣区域的边界仍然是一个现实的难题。完全自动化技术的经验表明,通常通常手动划定边界而不是纠正自动化错误。由于专家指导过程,因此半自动,用户指导的技术(如智能剪刀和活动轮廓模型)已被证明更有前途。本文将报告并比较另一种用户指导系统专家的跟踪助手的最新结果,该系统从专家那里学习边界定义,然后协助完成边界跟踪任务。学习的边界定义更好地重现了专家的行为,因为它不依赖于其他模型的先验边缘定义假设。

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