Automated Corneal Segmentation of Anterior Segment Photographed Images using Centroid-Based Active Contour Model

Daud, Marizuana Mat; Diyana, W. Mimi; Zaki, Wan Mimi Diyana Wan; Hussain, Aini; Mutalib, Haliza Abdul; Saad, Mohamad Hanif Md; Razak, Aishah Abdul

doi:10.1016/j.procs.2019.12.115

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“…This improvised method was proposed by Zhao et al [36] to extract the iris structure and has been applied in this work. This method is important, as this step is crucial to localize the corneal region to automate the proposed segmentation method, as detailed in [37].…”

Section: ) Corneal Segmentation Using a Modified Active Contour Modelmentioning

confidence: 99%

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

et al. 2020

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Keratoconus (KC) is a type of eye disease that involves the thinning of the corneal layer and a change in the semispherical shape of the cornea to a bulging cone shape when viewed laterally. KC is difficult to detect in the early stages of the disease, as the patient does not feel any pain. Hence, the development of a KC detection (KD) method using a digital image processing approach is needed for the early detection of KC so that physicians can provide patients with the subsequent treatment sooner. The objective of this study was to develop a method of KD using a camera from a smart device to capture anterior and lateral segment photographed eye images (A&LSPIs). A total of 280 images comprising 140 KC and 140 normal A&LSPIs were used in this study, and all images were validated by a qualified expert. First, the corneal area of both image views was segmented so the geometric features could be extracted using the automated modified active contour model and the semiautomated spline function for the anterior and lateral images, respectively. Then, the features were selected using infinite latent feature selection (ILFS) by identifying the feature rankings based on the graph weighting that was automatically learned by the probabilistic latent semantic analysis (PLSA). The results showed that the all-combined features, where the proposed and improved features were successfully top ranked, had 96.05% accuracy, 98.41% sensitivity and 93.65% specificity with the RFn=50 classifier, outperforming the 7-NNMaha and SVMRBF classifiers. In conclusion, this study successfully developed a keratoconus detection system based on fusion features using a digital image processing approach for A&LSPIs captured with a smartphone camera.INDEX TERMS Keratoconus, lateral segment photographed images, anterior segment photographed images, fusion features, digital image processing

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Section: ) Corneal Segmentation Using a Modified Active Contour Modelmentioning

confidence: 99%

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

et al. 2020

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View full text Add to dashboard Cite

show abstract

Automated Corneal Segmentation of Anterior Segment Photographed Images using Centroid-Based Active Contour Model

Cited by 1 publication

References 11 publications

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

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