Biomedical image mosaicking is a trending topic. It consists of computing a single large image from multiple observations and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur, or either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/feature-based methods perform poorly. In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mosaicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well.

中文翻译：

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基于置信度的生物医学图像镶嵌动态优化模型。

生物医学图像镶嵌术是一个热门话题。它包括根据多个观察值计算单个大图像，并且当所述观察值几乎不重叠或经受光照变化，分辨率差，模糊，或具有高纹理或主要是均匀的内容时，将成为一项具有挑战性的任务。由于此类挑战在生物医学图像中很常见，因此经典的基于关键点/功能的方法效果不佳。在本文中，我们提出了一个基于成对模板匹配以及受约束的，置信度驱动的全局优化策略的新框架，以解决微观生物医学图像镶嵌的问题。首先，我们提供实验结果，这些结果显示出主观水平上的显着改善。然后，我们描述了一种用于目标评估的新验证策略，该策略也显示了改进。