Automated Detection and Measurement of Corneal Haze and Demarcation Line in Spectral-Domain Optical Coherence Tomography Images

Dhaini, Ahmad R.; Chokr, Manal; Oud, Sara El; Fattah, Maamoun Abdul; Awwad, Shady T.

doi:10.1109/access.2018.2789526

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…The intersections of the images in the orthogonal planes can be used to recognize alignment reference points, using homogenous translational transform T by factors x, y, and z. Points in the orthogonal images in 2D can be converted into 3D points in the 3D plane using Equations ( 4) and (5).…”

Section: Reconstruction Of the 3d Imagementioning

confidence: 99%

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Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Mahmoud

Mengash

2021

Sensors

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This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.

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Section: Reconstruction Of the 3d Imagementioning

confidence: 99%

“…Other approaches of 3-D Cornea construction from Microscopy Images have been introduced [ 4 ]. In Reference [ 5 ], the authors introduced automated measurement of corneal haze in optical tomography images. In addition, feature fusion techniques have been utilized for Keratoconus diagnosis 2-D photographed images [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Mahmoud

Mengash

2021

Sensors

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“…Ahmad R. Dhaini et al,2018 [6] recommended that the first approach which employs analysis of image and machine learning , which able to detect and calculate haze of corneal, presence of separation stripe like demarcation line and its intensity in OCT images automatically. The programmed approach gives the consumer with data of haze and visual annotation method, which reflects the haze shape and haze position and existence of demarcation line in the area of cornea.…”

Section: Related Workmentioning

confidence: 99%

Improvement in Keratoconus Diagnosis Using Morpho-Geometric Variables With RNN Networks

Kanimozhi¹

2021

ITII

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There is an eye disease called Keratoconus (KC) which has potential to cause visual acuity loss; hence, it can be considered as disability due to its severity. There are some limitations in current method in detecting cornea region’s boarder edge. Primary objective for the paper need to identify the structural description of disease’ asymmetry with the help of Morpho-geometric parameters relates with the keratoconous eyes along by means of slight visual control. It also includes the application of Recurrent Neural Network (RNN) analysis which is sort of Neural Network in which previous step’s output are sent to present step as an input. In order to determine most prominent correlation, Stepwise Discriminant Function Analysis is used in analyzing output. The Prominent correlation was identified between thinnest point in the anterior deviation and thinnest point in the posterior deviations of minor keratoconic cases. MATLAB R2014 software is used to implement the framework and analyses of simulation results were performed.

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“…Arbelaez et al [27] implemented an SVM classifier for identifying KC in eyes using a combination of topographical map images from corneal topography and data from a Scheimpflug camera. Dhaini et al [28] proposed a new automated KD that employs image processing and machine learning methods to detect and measure corneal haze and demarcation lines in OCT images. Most recently, a study was conducted to mimic the concept of Placido's disk.…”

Section: Keratoconus Detection Using a Digital Image Processing Approachmentioning

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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Automated Detection and Measurement of Corneal Haze and Demarcation Line in Spectral-Domain Optical Coherence Tomography Images

Cited by 17 publications

References 31 publications

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Improvement in Keratoconus Diagnosis Using Morpho-Geometric Variables With RNN Networks

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

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