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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.


Segmented retinal and choroidal layers: (a) highly coherent 2D structure tensor IC(s, t), (b) binary map IB′(s, t) of highly coherent tensor, (c) canny edge detection of retinal and choroid layer, and (d) segmented retinal layers.
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fig5: Segmented retinal and choroidal layers: (a) highly coherent 2D structure tensor IC(s, t), (b) binary map IB′(s, t) of highly coherent tensor, (c) canny edge detection of retinal and choroid layer, and (d) segmented retinal layers.

Mentions: After extracting the highly coherent tensor IC(s, t), the binary map IB′(s, t) of IC(s, t) is computed using Otsu algorithm [29]. Afterwards, retinal layers are extracted from the digitalized map IB′(x, y) by computing retinal edges using canny edge detection [30], as shown in Figure 5.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Segmented retinal and choroidal layers: (a) highly coherent 2D structure tensor IC(s, t), (b) binary map IB′(s, t) of highly coherent tensor, (c) canny edge detection of retinal and choroid layer, and (d) segmented retinal layers.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5382397&req=5

fig5: Segmented retinal and choroidal layers: (a) highly coherent 2D structure tensor IC(s, t), (b) binary map IB′(s, t) of highly coherent tensor, (c) canny edge detection of retinal and choroid layer, and (d) segmented retinal layers.
Mentions: After extracting the highly coherent tensor IC(s, t), the binary map IB′(s, t) of IC(s, t) is computed using Otsu algorithm [29]. Afterwards, retinal layers are extracted from the digitalized map IB′(x, y) by computing retinal edges using canny edge detection [30], as shown in Figure 5.

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.