Abstract
Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure and medical devices. Therefore, in this paper, we pursued the development of a LSCD detection technique (LDT) utilizing image processing methods. Early diagnosis of LSCD is very crucial for physicians to arrange for effective treatment. In the proposed technique, we developed a method for LSCD detection utilizing frontal eye images. A dataset of 280 eye images of frontal and lateral LSCD and normal patients were used in this research. First, the cornea region of both frontal and lateral images is segmented, and the geometric features are extracted through the automated active contour model and the spline curve. While the texture features are extracted using the feature selection algorithm. The experimental results exhibited that the combined features of the geometric and texture will exhibit accuracy of 95.95%, sensitivity of 97.91% and specificity of 94.05% with the random forest classifier of n = 40. As a result, this research developed a Limbal stem cell deficiency detection system utilizing features’ fusion using image processing techniques for frontal and lateral digital images of the eyes.
Highlights
The corneal epithelium surface is always getting repopulated by limbal stem cells
In this paper, we pursued the development of a Limbal Stem Cell Deficiency (LSCD) detection technique (LDT) utilizing image processing methods
We developed a method for LSCD detection utilizing frontal eye images
Summary
The corneal epithelium surface is always getting repopulated by limbal stem cells. If a deficiency in these stem cells occur, visual impairment can happen. These features are extracted from frontal segment eye images. They utilized U-Net image segmentation CNN medical image segmentation to differentiate between healthy and sick corneas They measure their technique performance in CEC images with different corneal diseases. Fabijańska [15] utilized health status information of the endothelium extracted from the shape of the endothelial cornea cells in digital imagery They utilized U-Net CNN for image skeletonized segmentation. The first step is the preprocessing of the acquired images of the frontal and lateral views for cases with different stages of LSCD including cornea segmentation, the second step is the processing of the digital images to obtain features relevant to the disease (learning process).
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