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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

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.


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Unlabeled Duke dataset: (a) healthy classified scans; (b) ARMD classified scans.
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fig14: Unlabeled Duke dataset: (a) healthy classified scans; (b) ARMD classified scans.

Mentions: Figure 13 shows 7 randomly selected B-scans for each pathology from AFIO dataset that has been correctly classified. In each scan, ILM is shown in red color, choroid is shown in green color, and cyst or serous pathology is shown in yellow color. Apart from this, Figure 14 shows 6 randomly selected B-scans from Duke dataset that have been correctly classified. The color scheme remains the same as in Figure 13 except for yellow color which is used to show the extracted RPE layer in Figure 14.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Unlabeled Duke dataset: (a) healthy classified scans; (b) ARMD classified scans.
© Copyright Policy
Related In: Results  -  Collection

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

fig14: Unlabeled Duke dataset: (a) healthy classified scans; (b) ARMD classified scans.
Mentions: Figure 13 shows 7 randomly selected B-scans for each pathology from AFIO dataset that has been correctly classified. In each scan, ILM is shown in red color, choroid is shown in green color, and cyst or serous pathology is shown in yellow color. Apart from this, Figure 14 shows 6 randomly selected B-scans from Duke dataset that have been correctly classified. The color scheme remains the same as in Figure 13 except for yellow color which is used to show the extracted RPE layer in Figure 14.

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.


Related in: MedlinePlus