Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.
This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
- Alsaih, K; Lemaitre, G; Vall, JM; Rastgoo, M; Sidibe, D; Wong, TY; Lamoureux, E; Milea, D; Cheung, CY; Meriaudeau, F
- August 2016
- Annu Int Conf Ieee Eng Med Biol Soc
Volume / Issue
- 2016 /
Start / End Page
- 1344 - 1347
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)
- United States