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computer keyboard indicating which area flickered. This system is designed by professionals to detect the common glaucomatous defects.
Third, inter ocular pressure and fuzzy logic are applied. Increased IOP may damage the optic nerve and be responsible for visual loss. By using a set of fuzzy “IF-THEN” rules, a decision can be make as to whether a patient has glaucoma or not. For example,28,29,31
1.IF both the IOP and the Abnormal visual field test scores are “High,” THEN the Risk is “High”;
2.IF IOP is “High,” and the Abnormal visual field test scores are low, THEN the Risk is “Moderate”;
3.IF Family history is “Bad,” Age is “Old,” and the Abnormal visual field test scores are “High,” THEN the Risk is “High”;
4.IF Family history is “Bad,” Diabetes is “High,” and Abnormal visual field test scores are “High,” THEN the Risk is “High.”
This system is cost effective and appropriate for detecting early-stage glaucoma.
3.4.3.2. Computational system using different classifiers
The computational system for diagnosing glaucoma performs as it does when diagnosing diabetic retinopathy and cataracts. Figure 3.11 reiterates these steps.
3.4.4.Computational Decision Support System: Blepharitis, Rosacea, Sjögren, and Dry Eyes
Blepharitis is an eye disease similar to dry eye that is caused by inflammation in the eyelid margins. The inflammation causes eye redness, itching, and irritation. Dry eye is caused by decreased tear production or increased tear film evaporation.32,33 The symptoms, itchy, tired, painful, and red eyes, are similar to those caused by blepharitis. However, treatment for dry eye and blepharitis may differ. Thus, it is important for clinicians to differentiate between the diseases.
A computational system can aid in the classification of healthy, blepharitis, rosacea, Sjögren, and dry eyes through clustering analysis.33 When a clinician constructs the model for the classification of the images, he or
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Image Acquisition
Image Preprocessing (equalization and binarizing)
Feature Extraction (using PCA, ICA, and GLCM)
Feature Selection (using Chisquared, infogain, and RatioGain)
Image Classification (SVM and MLP)
Fig. 3.11. Computational steps for diagnosing glaucoma using classifiers.
she classes image labels to their respective categories (healthy or diseased eyes). Once the clinician determines the correct category, he or she will program the data to the classifiers to build a model. The model can then be used for the future prediction of images. The computational steps for this process are described in Fig. 3.12.
The first five steps (image acquisition through classification) have been explained in previous sections. Once the relevant features are selected or feature selection is performed, then the clinician will classify images. Classification can be performed using 10-fold cross-validation. In 10-fold crossvalidation, the dataset images are partitioned into 10 sub datasets. Of the
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Fig. 3.12. Computational steps for diagnosing glaucoma using clustering analysis.
10 datasets, a single dataset is used for testing the model, and the remaining nine datasets are used as training data. Once the algorithm is trained, the cross-validation process is repeated once with each of the 10 datasets as the new testing data. The 10 results from the folds then can be averaged to produce a single result.
A two-step process model construction and model usage predicts categorical class labels (discrete or nominal). Model construction classifies data based on the training set of images and uses this model to classify new images. In model construction, each sample or image is assumed to belong to a predefined class, and its procedure is described in Fig. 3.13. However, if the class label is unknown, then the clinician will perform clustering to learn the class labels before constructing a model as mention in Fig. 3.14.
Classification
Training Images Classifier (Model)
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Fig. 3.13. Model construction.
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Fig. 3.14. |
Class labeling. |
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Classification of Images |
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Fig. 3.15. Model usage.
Various clustering algorithms, such as K means, hierarchal, and DB scan, can be used in this step. By using a classification algorithm, the images of eyes that are healthy, that are contaminated with blepharitis, and that are dry can be classified into relevant groups that have common features or characteristics.
The classification model is used to classify future or unknown images. The known label of a test sample is compared with the classified result from the model, and an accuracy rate is calculated as mentioned in Fig. 3.15. The accuracy rate is the percentage of test images that are correctly classified by the model. To avoid over-fitting, the test set must be independent of the training set.
Model construction, class labeling, and model usage will create an automatic and interactive system that can classify the images of different categories using clustering and the 10-fold cross-validation system. This system will help the ophthalmologist to make a diagnostic decision.
3.4.4.1.Utility of bleb imaging with anterior segment OCT in clinical decision making
Recent studies have shown that bleb imaging with anterior segment OCT (ASOCT) can provide relevant information to an ophthalmologist that is not present in clinical evaluation.34 This information is especially useful for determining whether laser suture lysis (LSL) should be performed.
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