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Acuurate segmentation of retina nerve fiber layer in OCT images

机译:黄液中视网膜神经纤维层的准确细分

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The quantification of intra-retinal boundaries in the Optical Coherence Tomography (OCT) is a crucial task to study and diagnose neurological and ocular diseases. Since the manual segmentation of layers is usually a time consuming task and relies on the user, an excessive volume of research has been done to do this job automatically and without interference of the user. Although, generally the same procedure is applied to extract all layers, but finding the RNFL is typically more challenging due to the fact that it may vanish in some parts of the eye, especially close to the fovea. To have general software, besides using common methods such as applying the shortest path algorithm on the global gradient of an image, some extra steps have been taken here to narrow the search area for Dijstra's [8] algorithm, especially for the second boundary. The result demonstrates high accuracy in segmenting the RNFL that is really important for the diagnosing Glaucoma.
机译:在光学相干断层扫描(OCT)中的视网膜内边界的定量是研究和诊断神经系统和眼部疾病的关键任务。由于层的手动分割通常是耗时的任务并且依赖于用户,因此已经完成了过多的研究来自动执行此作业,而不会干扰用户。虽然通常相同的程序用于提取所有层,但是发现RNFL通常更具挑战性,因为它可能在眼睛的某些部分中消失,特别接近FOVEA。为了具有一般软件,除了使用常用方法,如在图像的全局梯度上应用最短路径算法,这里已经在此处采用了一些额外的步骤来缩小Dijstra [8]算法的搜索区域,尤其是对于第二边界。结果表明,对诊断青光眼非常重要的RNFL,展示了高精度。

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