Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Automated classification of basal cell carcinoma in mouse skin by polarization sensitive optical coherence tomography

Not Accessible

Your library or personal account may give you access

Abstract

An automated, support-vector-machine classifier was developed to discriminate basal cell carcinoma from healthy mouse skin images obtained with polarization sensitive optical coherence tomography. The results demonstrate that polarization sensitive optical coherence tomography has the potential to achieve accurate diagnosis of skin cancer noninvasively.

© 2014 Optical Society of America

PDF Article
More Like This
Towards automated detection of basal cell carcinoma from polarization sensitive optical coherence tomography images of human skin

Tahereh Marvdashti, Lian Duan, Katherine J. Ransohoff, Sumaira Z. Aasi, Jean Y. Tang, and Audrey K. Ellerbee
STh3K.3 CLEO: Science and Innovations (CLEO:S&I) 2015

Machine-learning detection of basal cell carcinoma in human skin using polarization sensitive optical coherence tomography

Tahereh Marvdashti, Lian Duan, Sumaira Z. Aasi, Jean Y. Tang, and Audrey K. Ellerbee Bowden
JM4A.5 Cancer Imaging and Therapy (Cancer) 2016

Which histological characteristics of basal cell carcinomas influence the quality of optical coherence tomography imaging?

M. Mogensen, L. Thrane, T.M Joergensen, B.M. Nürnberg, and G.B.E. Jemec
7372_1U European Conference on Biomedical Optics (ECBO) 2009

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved