Limits...
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.


Related in: MedlinePlus

Early self-diagnosis of macular syndromes: (a) early symptoms of RPE atrophy due to the presence of drusen; (b) early formation of cyst fluid within macular pathology. These scans are correctly identified by our proposed system.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5382397&req=5

fig15: Early self-diagnosis of macular syndromes: (a) early symptoms of RPE atrophy due to the presence of drusen; (b) early formation of cyst fluid within macular pathology. These scans are correctly identified by our proposed system.

Mentions: The proposed multilayered classification system is based on 9D feature vector, extracted from candidate OCT B-scan. At the first layer of our classification system, an automated decision between normal and abnormal retinal pathology is made by analyzing f1, f2, f3, and f4 as these features contain the objective evaluation of retinal thickness profile. If the candidate is classified as abnormal, then it is further classified as RE, CSCR, and ARMD. The discrimination between RE cysts and CSCR is provided by f5 features as RE cysts contain more energy as compared to CSCR. Also, the presence of drusen within the retinal and choroidal boundary is detected by atrophic analysis of RPE profile through f6, f7, f8, and f9 features. All of these features for 5 randomly selected subjects from each case are shown in Table 3. Apart from this, we have applied our proposed system on our local dataset acquired from AFIO and also publicly available Duke dataset. AFIO dataset contains samples of RE, CSCR, and healthy subjects while Duke dataset contains samples of RE, ARMD, and healthy subjects. Our proposed system correctly classifies a total of 2817/2819 retinal pathologies from both datasets. Detailed analysis of results is shown in Table 5. The misclassification of two healthy samples as diseased from AFIO dataset is because we have tuned our system in such a way to give more weightage to the correct classification of diseased samples as it is more critical to classify diseased samples accurately as compared to healthy subjects. Our proposed system is also computationally quite fast and it takes around a minute on average to give a complete disease diagnosis on a machine with 5th-generation core i5 CPU (2.2 GHz) and 4 GB DDR3 RAM. Our proposed system is quite robust and sensitive to retinal abnormalities as it can also detect small and early retinal abnormalities from OCT B-scan. Two of such cases are shown in Figure 15.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Early self-diagnosis of macular syndromes: (a) early symptoms of RPE atrophy due to the presence of drusen; (b) early formation of cyst fluid within macular pathology. These scans are correctly identified by our proposed system.
© Copyright Policy
Related In: Results  -  Collection

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

fig15: Early self-diagnosis of macular syndromes: (a) early symptoms of RPE atrophy due to the presence of drusen; (b) early formation of cyst fluid within macular pathology. These scans are correctly identified by our proposed system.
Mentions: The proposed multilayered classification system is based on 9D feature vector, extracted from candidate OCT B-scan. At the first layer of our classification system, an automated decision between normal and abnormal retinal pathology is made by analyzing f1, f2, f3, and f4 as these features contain the objective evaluation of retinal thickness profile. If the candidate is classified as abnormal, then it is further classified as RE, CSCR, and ARMD. The discrimination between RE cysts and CSCR is provided by f5 features as RE cysts contain more energy as compared to CSCR. Also, the presence of drusen within the retinal and choroidal boundary is detected by atrophic analysis of RPE profile through f6, f7, f8, and f9 features. All of these features for 5 randomly selected subjects from each case are shown in Table 3. Apart from this, we have applied our proposed system on our local dataset acquired from AFIO and also publicly available Duke dataset. AFIO dataset contains samples of RE, CSCR, and healthy subjects while Duke dataset contains samples of RE, ARMD, and healthy subjects. Our proposed system correctly classifies a total of 2817/2819 retinal pathologies from both datasets. Detailed analysis of results is shown in Table 5. The misclassification of two healthy samples as diseased from AFIO dataset is because we have tuned our system in such a way to give more weightage to the correct classification of diseased samples as it is more critical to classify diseased samples accurately as compared to healthy subjects. Our proposed system is also computationally quite fast and it takes around a minute on average to give a complete disease diagnosis on a machine with 5th-generation core i5 CPU (2.2 GHz) and 4 GB DDR3 RAM. Our proposed system is quite robust and sensitive to retinal abnormalities as it can also detect small and early retinal abnormalities from OCT B-scan. Two of such cases are shown in Figure 15.

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