Digital histology of tissue with Mueller microscopy and FastDBSCAN
Hee Ryung Lee, Christian Lotz, Florian Kai Groeber Becker, Sofia Dembski, Tatiana Novikova
Abstract
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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>></mml:mo> </mml:mrow> <mml:mn>97</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> ) compared to the original version of the algorithm.