figshare
Browse

Automatic and robust segmentation of endoscopic OCT images and optical staining

Posted on 2017-04-28 - 19:27
We report a generic method for automatic segmentation of endoscopic optical coherence tomography (OCT) images. In this method, OCT images are first processed with L1-L0 norm minimization based de-noising and smoothing algorithms to increase the signal-to-noise ratio (SNR) and enhance the contrast between adjacent layers. The smoothed images are then formulated into cost graphs based on their vertical gradients. After that, tissue-layer segmentation is performed with the shortest path search algorithm. The efficacy and capability of this method are demonstrated by automatically and robustly identifying all five interested layers of guinea pig esophagus from in vivo endoscopic OCT images. Furthermore, thanks to the ultrahigh resolution, high SNR of endoscopic OCT images and the high segmentation accuracy, this method permits in vivo optical staining histology and facilitates quantitative analysis of tissue geometric properties, which can be very useful for studying tissue pathologies and potentially aiding clinical diagnosis in real time.

CITE THIS COLLECTION

DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
or
Select your citation style and then place your mouse over the citation text to select it.

SHARE

email
need help?