Authors:
A. Gonzalez-Lopez
;
B. Remeseiro
;
M. Ortega
and
M. G. Penedo
Affiliation:
Universidade da Coruña, Spain
Keyword(s):
OCT, Retinal Images, Choroid, Texture Analysis, Pattern Recognition, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
Optical Coherence Tomography (OCT) is a widely extended imaging technique in the opthalmic field for diagnostic
purposes. Since layers composing retina can be identified in these images, several image processingbased
methods have been presented to segment them automatically in these images, with the aim of developing
medical-support applications. Recently, appearance of Enhanced Depth Imaging (EDI) OCT allows to tackle
exploration of the choroid which provides high information of eye processes. Therefore, segmentation of
choroid layer has become one of the more relevant problems tackled in this field, but it presents different features
that rest of the layers. In this work, a novel texture-based study is proposed in order to show that textural
information can be used to characterize this layer. A pattern recognition process is carried out by using different
descriptors and a process of classification, considering marks performed by two experts for validation.
Results show th
at characterization using texture features is effective with rates over 90% of success.
(More)