Taimur Hassan

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Automated segmentation of subretinal layers for the detection of macular edema

Macular edema (ME) is considered as one of the major indications of proliferative diabetic retinopathy and it is commonly caused due to diabetes. ME causes retinal swelling due to the accumulation of protein deposits within subretinal layers. Optical coherence tomography (OCT) imaging provides an early detection of ME by showing the cross-sectional view of macular pathology. Many researchers have worked on automated identification of macular edema from fundus images, but this paper proposes a fully automated method for extracting and analyzing subretinal layers from OCT images using coherent tensors. These subretinal layers are then used to predict ME from candidate images using a support vector machine (SVM) classifier. A total of 71 OCT images of 64 patients are collected locally in which 15 persons have ME and 49 persons are healthy. Our proposed system has an overall accuracy of 97.78% in correctly classifying ME patients and healthy persons. We have also tested our proposed implementation on spectral domain OCT (SD-OCT) images of the Duke dataset consisting of 109 images from 10 patients and it correctly classified all healthy and ME images in the dataset.

Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces

BACKGROUND AND OBJECTIVES Macular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. METHODS The proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. RESULTS In this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. CONCLUSION The proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.

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

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 worlds 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.

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

Rapid development in the field of ophthalmology has increased the demand of computer aided diagnosis of various eye diseases. Papilledema is an eye disease in which the optic disc of the eye is swelled due to an increase in intracranial pressure. This increased pressure can cause severe encephalic complications like abscess, tumors, meningitis or encephalitis, which may lead to a patients death. Although there have been several papilledema case studies reported from a medical point of view, only a few researchers have presented automated algorithms for this problem. This paper presents a novel computer aided system which aims to automatically detect papilledema from fundus images. Firstly, the fundus images are preprocessed by going through optic disc detection and vessel segmentation. After preprocessing, a total of 26 different features are extracted to capture possible changes in the optic disc due to papilledema. These features are further divided into four categories based upon their color, textural, vascular and disc margin obscuration properties. The best features are then selected and combined to form a feature matrix that is used to distinguish between normal images and images with papilledema using the supervised support vector machine (SVM) classifier. The proposed method is tested on 160 fundus images obtained from two different data sets i.e. structured analysis of retina (STARE), which is a publicly available data set, and our local data set that has been acquired from the Armed Forces Institute of Ophthalmology (AFIO). The STARE data set contained 90 and our local data set contained 70 fundus images respectively. These annotations have been performed with the help of two ophthalmologists. We report detection accuracies of 95.6% for STARE, 87.4% for the local data set, and 85.9% for the combined STARE and local data sets. The proposed system is fast and robust in detecting papilledema from fundus images with promising results. This will aid physicians in clinical assessment of fundus images. It will not take away the role of physicians, but will rather help them in the time consuming process of screening fundus images.

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

BIOMISA Retinal Image Database for Macular and Ocular Syndromes

Retinopathy is a collective group of macular and ocular syndromes that damages the human retina due to increased fluid pressure or hyperglycemia. The major forms of retinopathy include macular edema (ME), exudative or non-exudative age related macular degeneration (AMD) and glaucoma. Various eye testing techniques are being used by ophthalmologists to grade retinopathy. Furthermore, different researchers are developing fully autonomous systems to mass screen eye patients across the globe. However, to validate the performance of these systems, they must be tested on publicly available standardized datasets. Therefore, this paper presents a retinal image database containing high quality 64 fundus and 2497 OCT brightness scans (B-scans). The proposed dataset is first of its kind in providing detailed annotations of retinal hemorrhages, hard exudates, intra-retinal and sub-retinal fluids, drusen, retinal pigment epithelium (RPE) atrophy and cup to disc (CDR) ratios from both retinal fundus and OCT imagery. The proposed dataset is also compared with the publicly available databases where it outmatched them by providing high quality fundus and OCT scans along with detailed markings through which different researchers can automatically diagnose different pathological conditions of human retina.

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula

Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.

High resolution OCT image generation using super resolution via sparse representation

In this paper we propose a technique for obtaining a high resolution (HR) image from a single low resolution (LR) image -using joint learning dictionary - on the basis of image statistic research. It suggests that with an appropriate choice of an over-complete dictionary, image patches can be well represented as a sparse linear combination. Medical imaging for clinical analysis and medical intervention is being used for creating visual representations of the interior of a body, as well as visual representation of the function of some organs or tissues (physiology). A number of medical imaging techniques are in use like MRI, CT scan, X-rays and Optical Coherence Tomography (OCT). OCT is one of the new technologies in medical imaging and one of its uses is in ophthalmology where it is being used for analysis of the choroidal thickness in the eyes in healthy and disease states such as age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy and inherited retinal dystrophies. We have proposed a technique for enhancing the OCT images which can be used for clearly identifying and analyzing the particular diseases. Our method uses dictionary learning technique for generating a high resolution image from a single input LR image. We train two joint dictionaries, one with OCT images and the second with multiple different natural images, and compare the results with previous SR technique. Proposed method for both dictionaries produces HR images which are comparatively superior in quality with the other proposed method of SR. Proposed technique is very effective for noisy OCT images and produces up-sampled and enhanced OCT images.

A practical approach to OCT based classification of Diabetic Macular Edema

This paper addresses the problem of automatic classification of OCT images for identification of patients with DME versus normal subjects. In this paper a relativity simple and practical approach is proposed to exploit the information in OCT images for a robust classification of Diabetic Macular Edema (DME) using coherent tensors. From the retinal OCT scan top and bottom layers are extracted to find thickness profile. Cyst spaces are also segmented out from the normal and DME images. The features extracted from thickness profile and cyst are tested on Duke Dataset having 55 diseased and 53 normal OCT scans. Results reveal that SVM with Leave-one-Out gives the maximum accuracy of 79.65% with 7.6 standard deviation. However, experiments reveal that for the identification of DME, nearly same accuracy of 78.7% can be achieved by using a simple threshold which can be calculated using thickness variation of OCT layers. Moreover a comparison of the proposed algorithm on a standard dataset with other recently published work shows that our method gives the best classification performance.