Digital histology of tissue with Mueller microscopy and FastDBSCAN.
Journal
Applied optics
ISSN: 1539-4522
Titre abrégé: Appl Opt
Pays: United States
ID NLM: 0247660
Informations de publication
Date de publication:
10 Nov 2022
10 Nov 2022
Historique:
entrez:
6
1
2023
pubmed:
7
1
2023
medline:
11
1
2023
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 36606902
pii: 518902
doi: 10.1364/AO.473095
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM