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. 2017 Jun 7;16(1):68.
doi: 10.1186/s12938-017-0352-9.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Khaled Alsaih  1   2 , Guillaume Lemaitre  1 , Mojdeh Rastgoo  1 , Joan Massich  1 , Désiré Sidibé  1 , Fabrice Meriaudeau  3   4
Affiliations

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Khaled Alsaih et al. Biomed Eng Online. .

Abstract

Background: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.

Methods: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.

Results and conclusion: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

Keywords: BoW; Classification; DME detection; HoG; LBP; SD-OCT.

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Figures

Fig. 1
Fig. 1
The pipeline is composed of: (1) pre-processing, (2) feature extraction, (3) feature representation, and (4) feature classification
Fig. 2
Fig. 2
Example of SD-OCT preprocessed OCT images. (1) Original image, (2) OCT-denoised image, (3) OCT-flattened image, and (4) OCT-cropped image
Fig. 3
Fig. 3
Local mapping. Example of non-overlapping windows on 2D slices
See this image and copyright information in PMC

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