Yazar "Sarhan, Abdullah" seçeneğine göre listele

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    Approaches for early detection of glaucoma using retinal images: A performance analysis
    (Springer Science and Business Media Deutschland GmbH, 2020) Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda
    Sight is one of the most important senses for humans, as it allows them to see and explore their surroundings. Multiple ocular diseases damaging sight have been detected over the years such as glaucoma and diabetic retinopathy. Glaucoma is a group of diseases that can lead to blindness if left untreated. No cure for glaucoma exists apart from early detection and treatment by an ophthalmologist. Retinal images provide vital information about an eye’s health. On the basis of advancements in retinal images technology it is possible to develop systems that can analyze these images for better diagnosis. To test the efficiency of some of the developed techniques, we obtained the code for four different approaches and did a performance analysis using four public datasets. We investigated the results along with the analysis time. The outcomes of the study are approaches for glaucoma detection;behavior of glaucoma related approaches on retinal images with different ocular diseases;challenges faced when analyzing retinal images; andglaucoma risk factors.
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    Glaucoma detection using image processing techniques: A literature review
    (Elsevier, 2019) Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda
    The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows: • approaches to segment different objects from both 2D and 3D images; • approaches that may lead to encouraging results for glaucoma detection; • challenges faced by researchers; and • currently available retinal datasets and evaluation methods.
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    H-OCS: A hybrid optic cup segmentation of retinal images
    (Springer Science and Business Media Deutschland GmbH, 2021) Sarhan, Abdullah; Rokne, Jone; Alhajj, Reda
    Glaucoma is the second leading cause of irreversible vision loss. Early diagnosis and treatment can, however, slow the progression of the disease. Specialists making this diagnosis rely on several tests and examinations such as visual field tests and examinations of retinal images and optical coherence tomography images. One of the regions examined by specialists when checking for retinal conditions is the optic nerve head region, which is the brightest region in retinal images. Within this region, the ratio between the cup and the disc can be used when diagnosing for glaucoma. Calculating the cup–disc ratio requires the segmentation of both the disc and the cup from retinal images. In a previous paper, a method for segmenting the disc was proposed. Here another deep learning model, H-OCS, is proposed for segmenting the cup from retinal images. A customized InceptionV3 model with transfer learning and image augmentation is used. Additionally, the output of H-OCS is refined and enhanced using a series of post-processing steps. H-OCS is tested on six publicly available datasets: RimOneV3, Drishti, Messidor, Refuge, Riga, and Magrebia and several ablation studies are conducted to evaluate the effectiveness of the proposed approach. Additionally, the performance of H-OCS is compare with other studies. An overall average accuracy of 97.86%, DC of 88.37%, Sensitivity of 89.09% and IoU of 79.66% was achieved.
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    Transfer learning through weighted loss function and group normalization for vessel segmentation from retinal images
    (Institute of Electrical and Electronics Engineers Inc., 2021) Sarhan, Abdullah; Rokne, Jon G.; Alhajj, Reda; Crichton, Andrew C.S.
    The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels that uses deep learning along with transfer learning. We adapted the U-Net structure to use a customized InceptionV3 as the encoder and used multiple skip connections to form the decoder. Moreover, we used a weighted loss function to handle the issue of class imbalance in retinal images. Furthermore, we contributed a new dataset to this field. We tested our approach on six publicly available datasets and a newly created dataset. We achieved an average accuracy of 95.60% and a Dice coefficient of 80.98%. The results obtained from comprehensive experiments demonstrate the robustness of our approach to the segmentation of blood vessels in retinal images obtained from different sources. Our approach results in greater segmentation accuracy than other approaches.
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    Utilizing a responsive web portal for studying disc tracing agreement in retinal images
    (Public Library of Science, 2021) Sarhan, Abdullah; Swift, Andrew J.; Gorner, Adam T.; Rokne, Jon G.; Alhajj, Reda; Docherty, Gavin; Crichton, Andrew C.S.
    Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.
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    Utilizing digitized surveys for data collection: The case of developing countries
    (Springer Science and Business Media Deutschland GmbH, 2021) Sarhan, Abdullah; Addam, Omar; Rokne, Jone; Alhajj, Reda
    A major concern for public and private organizations worldwide is to ensure that its constituents are healthy. However, the degree to which this is ensured differ from country to country and even from community/individual to community/individual based on the level of wealth/income, education, etc. In developing countries the concern for the health of its population is particularly important the populations of since these countries often suffer from serious health issues. These health issues have attracted the attention of international organizations who have been focusing on raising awareness of the health issues within the local communities through education. Assessment of the status of the health of a population is dependent on data. This data is used to determine the allocation of the resources that are used to improve the health of the population. Traditionally, organizations have used paper surveys for collecting such data. Since this is a manual process it is prone to errors that also consumes a significant amount of time and effort. It is possible to overcome the limitations of paper surveys by using advanced technology for the collection process. This leads to digitized surveys which may be conducted using hand-held devices. Hand held devices can help getting more reliable and secure data in less time, with lower cost and with less effort. This paper contributes to these efforts by proposing a hand held device framework that has a user-friendly visual interface suitable for data collection in the field. The proposed framework has been implemented and used for the first time in Uganda. It has been well received by domain experts who showed that it was very successful and it was capable of collecting reliable data in less time than when they were using the paper-based process. We describe various components of the proposed framework along with the data storing feature for in-the-field accumulation of data. This was combined with after hours bulk data transmittal of the accumulated data from central points to a data repository.
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    Utilizing transfer learning and a customized loss function for optic disc segmentation from retinal images
    (Springer Science and Business Media Deutschland GmbH, 2021) Sarhan, Abdullah; Al-Khaz’Aly, Ali; Gorner, Adam; Swift, Andrew J.; Rokne, Jon G.; Alhajj, Reda S.; Crichton, Andrew C.S.
    Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78% and a Dice coefficient of 94.73% for a disc segmentation from a retinal image in 0.03 s. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.