Registration and restoration of Adaptive-Optics corrected retinal images
Registration and restoration of Adaptive-Optics corrected retinal images
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自适应光学校正视网膜图像的配准和恢复
DOI：
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发表时间：
2014
期刊：
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通讯作者：
M. Pâques
中科院分区：
文献类型：
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作者：
L. Blanco;L. Mugnier;A. Bonnefois;M. Pâques
原始的个人自适应光学校正泛光照明视网膜图像通常是相当嘈杂的，因为安全通量的限制。这些泛光照明图像的对比度也很差。因此，如果没有适当的后处理，这些图像的解释是困难的，后处理通常包括将记录的图像堆栈配准到马赛克图像中并恢复后者。我们已经开发了一种MAP框架中的图像配准方法，基于天文成像中的先前工作，并针对视网膜成像的具体情况定制，更准确地说，这是由于视网膜的照明和仪器的透射是不均匀的，这使得传统的配准方法可能失败。然后必须对马赛克图像进行去卷积，以便在视觉上恢复自适应光学带来的高分辨率。为此，我们执行一个无监督的近视去卷积，考虑到被成像的对象的3D性质。我们成功地将这整个处理链应用于视网膜血管的实验性体内图像。
Raw individual Adaptive-Optics-corrected flood-illuminated retinal images are usually quite noisy because of safety flux limitations. These flood-illuminated images are also of poor contrast. Interpretation of such images is therefore difficult without an appropriate post-processing, which typically includes the registration of the recorded image stack into a mosaic image and the restoration of the latter.We have developed an image registration method in a MAP framework, based on previous work in astronomical imaging, and tailored for the specifics of retinal imaging, more precisely to the fact that the illumination of the retina and the transmission of the instrument is non-homogeneous, which makes conventional registration methods likely to fail. The mosaic image must then be deconvolved in order to visually restore the high-resolution brought by adaptive optics. To this aim, we perform an unsupervised myopic deconvolution that takes into account the 3D nature of the object being imaged. We successfully apply this whole processing chain to experimental in vivo images of retinal vessels.