Title
Fully automated calcium detection using optical coherence tomography.
Abstract
Optical Coherence Tomography (OCT) is a new invasive technology for performing high-resolution cross-sectional imaging of the coronary arteries. In OCT images only Calcified plaque (CA) components can be accurately depicted as light penetrates hard tissue. In this work we present an automated method for detecting CA in OCT images. The method is fully automated as no user intervention is needed and includes three steps. In the first step the region between the lumen and the maximum penetration depth of OCT from the lumen border is determined. In the second step the region is classified into 3 clusters using the K-means algorithm. CA is identified using the results of k-means. The method was validated using experts' annotations on 27 images. The sensitivity of the method is 83% with Positive predictive value (PVV) 74 %.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6609779
EMBC
Keywords
Field
DocType
sensitivity,optical tomography,maximum penetration depth,calcified plaque components,coronary arteries,diseases,high-resolution cross-sectional imaging,experts annotations,optical coherence tomography,biomedical optical imaging,lumen border,image resolution,blood vessels,calcium,clusters classification,k-means algorithm,hard tissue,invasive technology,light penetration,image classification,fully automated calcium detection,positive predictive value,medical image processing,oct images,tomography,k means algorithm,coherence,optical imaging
Computer vision,Optical coherence tomography,Computer science,Penetration depth,Hard tissue,Lumen (unit),Artificial intelligence,Optical tomography,Contextual image classification,Image resolution
Conference
Volume
ISSN
Citations 
2013
1557-170X
2
PageRank 
References 
Authors
0.47
1
11