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Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images

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ABSTRACT

Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

No MeSH data available.


Training phase of SVM.
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fig11: Training phase of SVM.

Mentions: The classification system in our proposed system uses multilayered SVM classifier to distinguish between retinal abnormalities. After processing the input candidate OCT scan, 9 distinct features are extracted which are fused together to form a 9D feature vector f = {f1, f2, f3, f4, f5, f6, f7, f8, f9}. The feature vector f is then passed to the multilayered supervised SVM classifier for automated disease diagnosis. The classification system in our proposed system is trained on our custom prepared training dataset that includes 40 labeled images (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). The dataset has been annotated by multiple expert ophthalmologists. The first 5 features in the feature set are extracted from retinal thickness and cyst profile. The remaining 4 features are acquired by analyzing atrophy within the RPE profile. SVM is being incorporated in our proposed classification system because it is one of the fastest and accurate classifiers [31]. In our proposed system, SVM has a nonlinear decision boundary because of Gaussian radial basis function (RBF) and multilayer perceptron (MLP) kernel. Figure 11 demonstrates the training phase of our proposed classification system.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Training phase of SVM.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382397&req=5

fig11: Training phase of SVM.
Mentions: The classification system in our proposed system uses multilayered SVM classifier to distinguish between retinal abnormalities. After processing the input candidate OCT scan, 9 distinct features are extracted which are fused together to form a 9D feature vector f = {f1, f2, f3, f4, f5, f6, f7, f8, f9}. The feature vector f is then passed to the multilayered supervised SVM classifier for automated disease diagnosis. The classification system in our proposed system is trained on our custom prepared training dataset that includes 40 labeled images (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). The dataset has been annotated by multiple expert ophthalmologists. The first 5 features in the feature set are extracted from retinal thickness and cyst profile. The remaining 4 features are acquired by analyzing atrophy within the RPE profile. SVM is being incorporated in our proposed classification system because it is one of the fastest and accurate classifiers [31]. In our proposed system, SVM has a nonlinear decision boundary because of Gaussian radial basis function (RBF) and multilayer perceptron (MLP) kernel. Figure 11 demonstrates the training phase of our proposed classification system.

View Article: PubMed Central - PubMed

ABSTRACT

Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

No MeSH data available.