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Figures and Topics from this paper. References Publications referenced by this paper. Revised formulas for summarizing retinal vessel diameters. Michael D. Knudtson , Kristine E. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Sinthanayothin , James Frederick Boyce , H. Cook , Thomas H. An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus.

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Gabor features Gabor filters can be convolved with the image at different frequencies and orientations which can generate different feature channels for image classification [ 36 ]. The DWT features captures both spatial and frequency information of the image. DWT analyses the image by decomposing it into a coarse approximation via low-pass filtering and into detail information via high-pass filtering. Such decomposition is performed recursively on low-pass approximation coefficients obtained at each level [ 38 ].

The image is divided into four bands i. As an example, LH indicates that rows and columns are filtered with low pass and high pass filters, respectively. DWT decomposition is calculated on five different wavelet families i. For a particular region in the optic disc cropped image, we can calculate two types of features using these bands i. After determination of feature matrix, the feature matrix is normalized using z-score normalization [ 39 ]. Due to division of optic disc cropped images into five regions, the number of features generated is five times larger than the situations where features are generated for a whole image.

For fundus images, regional features out of features have been significant towards glaucoma classification and regional features have been significant towards classification in SLO images.

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The bar plot shows that the textural and gabor features can be more clinically significant compared to other types of features. We have run the wrapper feature selection with the performance measures mentioned previously on the significant features. The results of feature selection procedure have been shown in Fig. The results shows that if the features are selected by AUC as performance measure of wrapper feature selection, we can achieve significantly higher classification accuracy compared to other performance measures.

Also the classification power of regional features have been significantly better compared to global features both in case of fundus and SLO images. The list has mostly been dominated by either textural or Gabor features. Table 2 Comparison of number of features selected by each feature selection methods from different regions and total number of features selected. Table 3 Symbols of features selected by sequential maximization approach. Table 4 Input parameters for the classifiers. On selected regional image features, we have constructed the binary classifier for glaucoma classification using Support Vector Machines SVM [ 42 ].

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In traditional SVM, two parallel planes are generated such that each plane is as far apart as possible however in non-parallel SVM, the condition of parallelism is dropped. Evaluation metrics For optic disc segmentation performance, we have Dice Coefficient [ 45 ] as an evaluation measurement, which is the degree of overlap of two regions.

We have conducted experimental evaluation on both fundus and SLO image datasets from three aspects: 1 Optic disc segmentation accuracy performance. Accuracy performance comparison with either geometric or non-geometric methods. We have compared our segmentation methods with clinical annotations and existing models such as Active Shape Model [ 48 ], Chan-Vese [ 49 ]. The experimental results are shown in Figs. Also some of the examples of optic disc segmentation compared to clinical annotations has been shown in Fig.

The visual results show that segmentation accuracy is quite comparable to clinical annotation; especially in the right column which represent the examples of glaucomatous optic disc with PPA.

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Table 5 Accuracy comparison of the proposed optic disc segmentation approach with our previous approach. According to the results, the feature sets selected by AUC maximization have higher accuracy on both cross-validation sets and the test sets compared to the ones selected by maximization of linear and quadratic classification accuracy.

The results also show that dropping the parallelization condition from the SVM can have marginal improvement in terms of classification accuracy; like in case of Twin SVM. Moreover, classifier with linear specifications i. The results show that the non-linear classifiers such as RBF-SVM and QDA have high false negatives compared to their linear counterparts which have resulted the depreciation in their performance.

The Twin SVM classifier has achieved the accuracy of Table 6 Comparison of classification accuracies across different feature selection methods in cross-validation set. Table 7 Comparison of classification accuracies across different feature selection methods in test set. Table 8 Comparison of sensitivity, specificity and accuracy across different classifiers. To validate our proposed method, we have compared the performance of RIFM with 1 geometrical based clinical indicators on glaucoma such as vertical and horizontal CDRs, vasculature shift, and 2 the existing methods using non-geometrical global features [ 15 , 18 , 19 ].

In case of geometrical indicators, both vertical as well as horizontal CDR has been clinically annotated for both fundus and SLO images whereas vasculature shift has been determined automatically using the method mentioned in [ 50 ].

The cutoff value for both CDRs is set to 0. In case of non-geometrical features, we have calculated global image features under the same procedure as in case of regional features except that they are calculated for whole optic disc cropped image. Like regional features, we have constructed a global image feature model under Twin SVM on the features selected by wrapper-AUC approach under the classifier parameters where global features performed the best. The performance comparison is shown with respect to ROC curves in Fig. Table 9 Accuracy comparison of the proposed RIFM model with either geometric or non-geometric-based methods.

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Login or Create Account. Allow All Cookies. Brennan, "Anatomically accurate, finite model eye for optical modeling," J. A 14 , Not Accessible Your account may give you access. Abstract There is a need for a schematic eye that models vision accurately under various conditions such as refractive surgical procedures, contact lens and spectacle wear, and near vision.

Multiconjugate adaptive optics applied to an anatomically accurate human eye model P. Wide-field schematic eye models with gradient-index lens Alexander V. More Recommended Articles. Accommodation-dependent model of the human eye with aspherics R. Wide-field optical model of the human eye with asymmetrically tilted and decentered lens that reproduces measured ocular aberrations James Polans, Bart Jaeken, Ryan P.

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