Review of OCT and fundus images for detection of Macular Edema

Hassan, Taimur; Akram, M. Usman; Hassan, Bilal; Nasim, Ammara; Bazaz, Shafaat A.

doi:10.1109/ist.2015.7294517

Cited by 39 publications

(20 citation statements)

References 7 publications

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“…General steps of detection include image pre-processing, localization and masking of OD (optic disc), fovea detection, extraction of ROI, motion pattern generation, and severity of disease determination. A major problem faced for detection of DME is that color of optic disc and exudates is the same thus optic disc is masked to avoid considering optic disc as exudates [5].…”

Section: A Diabetic Retinopathymentioning

confidence: 99%

“…Many methods have been developed for measuring CDR. Common steps used for measuring CDR are: (i) Pre-processing of image (ii) Determination of Region of Interest (ROI) and (iii) Localization of OD and optic-cup (OC) [5]. Preprocessing of the image may include illumination correction (enhancing the contrast and removing blurring effect) and removal of blood vessels.…”

Section: B Glaucomamentioning

confidence: 99%

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Retinal disorder as a biomarker for detection of human diseases

Malik

Batool

Rubab

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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The rapid development of digital imaging and computer vision has extended the potential of using these technologies in the diagnosis of human diseases. Medical image analysis is a non-invasive way to identify human diseases which are usually manually marked by medical experts or automatically detected by the Computer-Aided Diagnostic (CAD) system. The fundus images provide facts and information about visual disorders (diabetic retinopathy, glaucoma, age-related macular degeneration, etc.), brain disease (Alzheimer's), and heart-related diseases (cardiovascular, hypertension, and stroke). Extensive research has been done in the last two decades in developing automated methods for the identification of various human disorders from fundus images. This survey paper provides the reader with a broad review of the fundus image processing for the identification of various human disorders. In this paper, human diseases that can be diagnosed through fundus images are reviewed. Several publicly available fundus datasets which are helpful in the screening of these abnormalities are briefly discussed. This work aims to show the importance of retinal images in the diagnosis of different human diseases. The main challenges and difficulties faced in effective diagnosis are presented and discussed. We believe that this work will guide the researchers in addressing the challenges in existing solutions and assist doctors in the proper diagnosis of various diseases.

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Section: A Diabetic Retinopathymentioning

confidence: 99%

Section: B Glaucomamentioning

confidence: 99%

Retinal disorder as a biomarker for detection of human diseases

Malik

Batool

Rubab

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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“…The two non-invasive imaging modalities that are clinically in practice for retinal examination, are OCT and fundus imagery [8]. OCT captures the tissue reflection through light coherency.…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

Hassan

et al. 2019

Sensors

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Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

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“…The major benefit of OCT over other eye testing techniques is that it can give an objective and accurate visualization of early retinal syndromes [7]. OCT imaging works on the principle of Michelson interferometer where a beam splitter decomposes low coherence light source into two parts.…”

Section: Introductionmentioning

confidence: 99%

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

Khalid

Akram

Hassan

et al. 2017

BioMed Research International

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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.

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Review of OCT and fundus images for detection of Macular Edema

Cited by 39 publications

References 7 publications

Retinal disorder as a biomarker for detection of human diseases

Retinal disorder as a biomarker for detection of human diseases

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

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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