Classifying DME vs normal SD-OCT volumes: A review
- Resource Type
- Conference
- Authors
- Massich, Joan; Rastgoo, Mojdeh; Lemaitre, Guillaume; Cheung, Carol Y.; Wong, Tien Y.; Sidibe, Desire; Meriaudeau, Fabrice
- Source
- 2016 23rd International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2016 23rd International Conference on. :1297-1302 Dec, 2016
- Subject
- Bioengineering
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Feature extraction
Principal component analysis
Retina
Diabetes
Pathology
Histograms
Testing
Diabetic Macular Edema (DME)
Spectral Domain OCT (SD-OCT)
Machine Learning (ML)
benchmark
- Language
This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison.