Paper Info
Title
Automated foveola localization in retinal 3D-OCT images using structural support vector machine prediction.
Abstract
We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.
Year
DOI
Venue
2012
10.1007/978-3-642-33415-3_38
MICCAI
Keywords
Field
DocType
structural support vector machine,localization error,foveola location,zero-one loss,ground truth position,localization problem,automated method,s-svm formulation,structural support,associated localization loss,automated foveola localization,support vector machines,algorithms,artificial intelligence,diagnostic imaging
Computer vision,Binary classification,Pattern recognition,Medical imaging,Computer science,Local binary patterns,Support vector machine,Software,Ground truth,Artificial intelligence,Foveola,Landmark
Conference
Volume
Issue
ISSN
15
Pt 1
0302-9743
Citations 
PageRank 
References 
1
0.35
9
Authors
6
Name
Order
Citations
PageRank
Yu-Ying Liu1353.04
ishikawa251530.86
Mei Chen341836.25
Gadi Wollstein4576.02
Joel S Schuman5638.75
James M. Rehg65259474.66