Nonlinear Optical Microscopy Signal Processing Strategies in Cancer

Authors
Adur, Javier Fernando; Carvalho, Hernandes F.; Cesar, Carlos L.; Casco, Victor Hugo
Publication Year
2014
Language
English
Format
article
Status
Published version
Description
This work reviews the most relevant present-day processing methods used to improve the accuracy of multimodal nonlinear images in the detection of epithelial cancer and the supporting stroma. Special emphasis has been placed on methods of non linear optical (NLO) microscopy image processing such as: second harmonic to autofluorescence ageing index of dermis (SAAID), tumor-associated collagen signatures (TACS), fast Fourier transform (FFT) analysis, and gray level co-occurrence matrix (GLCM)-based methods. These strategies are presented as a set of potential valuable diagnostic tools for early cancer detection. It may be proposed that the combination of NLO microscopy and informatics based image analysis approaches described in this review (all carried out on free software) may represent a powerful tool to investigate collagen organization and remodeling of extracellular matrix in carcinogenesis processes.
Fil: Adur, Javier Fernando. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carvalho, Hernandes F.. Instituto Nacional de Ciéncia y Tecnologia de Fotónica Aplicada á Biología Celular; Brasil
Fil: Cesar, Carlos L.. Instituto Nacional de Ciéncia y Tecnologia de Fotónica Aplicada á Biología Celular; Brasil
Fil: Casco, Victor Hugo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Subject
nonlinear signal
nonlinear microscopy
anisotropy, gray level co-occurrence matrix
tumor-associated collagen signatures
Inmunología
Medicina Básica
CIENCIAS MÉDICAS Y DE LA SALUD
Access level
Open access
License
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/35434