In Vivo Micro-Image Mosaicing
In Vivo Micro-Image Mosaicing
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DOI：
10.1109/tbme.2010.2085082
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发表时间：
2011-01-01
影响因子：
4.6
通讯作者：
Salisbury, J. Kenneth
中科院分区：
文献类型：
--
作者：
Loewke, Kevin E.;Camarillo, David B.;Salisbury, J. Kenneth
光学成像的最新进展已经导致微型显微镜的发展，该微型显微镜可以被带到患者处用于可视化体内组织结构。这些设备有可能通过用体内病理学取代组织活检来彻底改变医疗保健。然而，这些显微镜的主要限制之一是受限的视场可能使图像解释和导航变得困难。在本文中，我们表明，图像拼接可以是一个强大的工具，扩大视野和创建图像地图的显微解剖结构。首先，我们提出了一个有效的算法，成对的图像拼接，可以实现在真实的时间。然后，我们解决了两个主要的挑战与医学应用中的图像拼接：累积图像配准误差和场景变形。为了处理累积误差，我们提出了一个全局对齐算法，利用概率机器人技术中常用的技术。为了适应场景变形，我们提出了一个局部对齐算法，将可变形的表面模型的镶嵌框架。这些算法被证明在体内与各种成像设备，包括手持双轴共聚焦显微镜，微型双光子显微镜，和市售的共聚焦显微内窥镜采集的图像序列。
Recent advances in optical imaging have led to the development of miniature microscopes that can be brought to the patient for visualizing tissue structures in vivo. These devices have the potential to revolutionize health care by replacing tissue biopsy with in vivo pathology. One of the primary limitations of these microscopes, however, is that the constrained field of view can make image interpretation and navigation difficult. In this paper, we show that image mosaicing can be a powerful tool for widening the field of view and creating image maps of microanatomical structures. First, we present an efficient algorithm for pairwise image mosaicing that can be implemented in real time. Then, we address two of the main challenges associated with image mosaicing in medical applications: cumulative image registration errors and scene deformation. To deal with cumulative errors, we present a global alignment algorithm that draws upon techniques commonly used in probabilistic robotics. To accommodate scene deformation, we present a local alignment algorithm that incorporates deformable surface models into the mosaicing framework. These algorithms are demonstrated on image sequences acquired in vivo with various imaging devices including a hand-held dual-axes confocal microscope, a miniature two-photon microscope, and a commercially available confocal microendoscope.