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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.


Detailed step-by-step flow diagram of the proposed system.
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fig3: Detailed step-by-step flow diagram of the proposed system.

Mentions: An autonomous decision support system is proposed here for the automated self-diagnosis of RE, CSCR, and ARMD pathology from OCT images. At first, the input OCT scan I(s, t) is loaded into our proposed system which is denoised using adaptive Wiener filter. The objective of denoising the candidate scan is to increase the sparsity within intraretinal pathology. After denoising the candidate scan, we extracted intraretinal layers to discriminate between normal and abnormal retinal pathology. These retinal and choroidal layers are segmented by computing a highly coherent tensor representation of macular pathology [25]. Extracted inner limiting membrane (ILM) and choroidal layer are used to compute cyst pathology within the candidate scan. Drusen within the retinal and choroidal boundary are detected by extracting RPE and measuring atrophy and retinal degeneration. After that, a 9D feature vector is obtained based on retinal thickness and cyst profile, RPE atrophic profile, and drusen. The feature vector is then passed to the trained multilayered SVM classifier to diagnose the retinal syndrome. Figure 3 shows the block diagram of our proposed system.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Detailed step-by-step flow diagram of the proposed system.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Detailed step-by-step flow diagram of the proposed system.
Mentions: An autonomous decision support system is proposed here for the automated self-diagnosis of RE, CSCR, and ARMD pathology from OCT images. At first, the input OCT scan I(s, t) is loaded into our proposed system which is denoised using adaptive Wiener filter. The objective of denoising the candidate scan is to increase the sparsity within intraretinal pathology. After denoising the candidate scan, we extracted intraretinal layers to discriminate between normal and abnormal retinal pathology. These retinal and choroidal layers are segmented by computing a highly coherent tensor representation of macular pathology [25]. Extracted inner limiting membrane (ILM) and choroidal layer are used to compute cyst pathology within the candidate scan. Drusen within the retinal and choroidal boundary are detected by extracting RPE and measuring atrophy and retinal degeneration. After that, a 9D feature vector is obtained based on retinal thickness and cyst profile, RPE atrophic profile, and drusen. The feature vector is then passed to the trained multilayered SVM classifier to diagnose the retinal syndrome. Figure 3 shows the block diagram of our proposed 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.