Digital histology of tissue with Mueller microscopy and FastDBSCAN

Author:

Lee Hee Ryung1,Lotz Christian2,Kai Groeber Becker Florian2,Dembski Sofia23,Novikova Tatiana4ORCID

Affiliation:

1. Korea Institute of Science and Technology

2. Fraunhofer Institute for Silicate Research ISC

3. University Hospital Würzburg

4. Florida International University

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 ( > 97 % ) compared to the original version of the algorithm.

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering

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