Abstract
Cone cell identification is essential for diagnosing and studying eye diseases. In this paper, we propose an automated cone cell identification method that involves TV-L1 optical flow estimation and K-means clustering. The proposed algorithm consists of the following steps: image denoising based on TV-L1 optical flow registration, bias field correction, cone cell identification based on K-means clustering, duplicate identification removal, identification based on threshold segmentation, and merging of closed identified cone cells. Compared with manually labelled ground-truth images, the proposed method shows high effectiveness with precision, recall, and F1 scores of 93.10%, 94.97%, and 94.03%, respectively. The method performance is further evaluated on adaptive optics scanning laser ophthalmoscope images obtained from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy. The evaluation results demonstrate that the proposed method can accurately identify cone cells in subjects with healthy retinas and retinal diseases.
Highlights
High-resolution in vivo retinal imaging facilitates the diagnosis and study of retinal diseases
The identification of cone cells can be implemented by using adaptive optics scanning laser ophthalmoscope (AO-SLO) images
We further evaluated the identification performance of the proposed method for cone cell identification of AO-SLO images from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy, as far as we know for the first time
Summary
High-resolution in vivo retinal imaging facilitates the diagnosis and study of retinal diseases. The identification of cone cells (one type of photoreceptor found in the retina) can be implemented by using adaptive optics scanning laser ophthalmoscope (AO-SLO) images. Sci. 2021, 11, 2259 provides high performance [36] This registration method requires preprocessing that increases its complexity. We adopt TV-L1 optical flow [37] to achieve direct software-based AO-SLO image registration. We further evaluated the identification performance of the proposed method for cone cell identification of AO-SLO images from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy, as far as we know for the first time
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