Automatic Identification of Intraretinal Cystoid Regions in Optical Coherence Tomography
Optical Coherence Tomography (OCT) is, nowadays, one of the most referred ophthalmological imaging techniques. OCT imaging offers a window to the eye fundus in a non-invasive way, permitting the inspection of the retinal layers in a cross sectional visualization. For that reason, OCT images are frequently used in the analysis of relevant diseases such as hypertension or diabetes. Among other pathological structures, a correct identification of cystoid regions is a crucial task to achieve an adequate clinical analysis and characterization, as in the case of the analysis of the exudative macular disease.
This paper proposes a new methodology for the automatic identification of intraretinal cystoid fluid regions in OCT images. Firstly, the method identifies the Inner Limitant Membrane (ILM) and Retinal Pigment Epithelium (RPE) layers that delimit the region of interest where the intraretinal cystoid regions are placed. Inside these limits, the method analyzes windows of a given size and determine the hypothetical presence of cysts. For that purpose, a large and heterogeneous set of features were defined to characterize the analyzed regions including intensity and texture-based features. These features serve as input for representative classifiers that were included in the analysis.
The proposed methodology was tested using a set of 50 OCT images. 502 and 539 samples of regions with and without the presence of cysts were selected from the images, respectively. The best results were provided by the LDC classifier that, using a window size of \(61 \times 61\) and 40 features, achieved satisfactory results with an accuracy of 0.9461.
KeywordsComputer-aided diagnosis Retinal imaging Optical Coherence Tomography Intraretinal cystoid regions
This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project.
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