Lee Hee Ryung, Lotz Christian, Kai Groeber Becker Florian, Dembski Sofia, Novikova Tatiana
Appl Opt. 2022 Nov 10;61(32):9616-9624. doi: 10.1364/AO.473095.
We present the results of the automated post-processing of Mueller microscopy images of skin tissue models with a new fast version of the algorithm of density-based spatial clustering of applications with noise (FastDBSCAN) and discuss the advantages of its implementation for digital histology of tissue. We demonstrate that using the FastDBSCAN algorithm, one can produce the diagnostic segmentation of high resolution images of tissue by several orders of magnitude faster and with high accuracy (>97) compared to the original version of the algorithm.
我们展示了使用一种新的快速密度空间聚类应用噪声算法（FastDBSCAN）对皮肤组织模型的穆勒显微镜图像进行自动后处理的结果，并讨论了其在组织数字组织学中的实现优势。我们证明，与该算法的原始版本相比，使用FastDBSCAN算法可以将组织高分辨率图像的诊断分割速度提高几个数量级，且准确率很高（>97）。
