Retinopathy

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Retinopathy is a general term that refers to some form of non-inflammatory damage to the retina of the eye. Frequently, retinopathy is an ocular manifestation of systemic disease. (Wikipedia.org)






Conferences related to Retinopathy

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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.


2019 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2019, the 26th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

Circuits and Systems, Computers, Information Technology, Communication Systems, Control and Instrumentation, Electrical Power Systems, Power Electronics, Signal Processing


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


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Periodicals related to Retinopathy

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Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Engineering in Medicine and Biology Magazine, IEEE

Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


Instrumentation and Measurement, IEEE Transactions on

Measurements and instrumentation utilizing electrical and electronic techniques.


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Most published Xplore authors for Retinopathy

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Xplore Articles related to Retinopathy

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A divide et impera strategy for automatic classification of retinal vessels into arteries and veins

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

The first pathologic alterations of the retina are seen in the vessel network. These modifications affect very differently arteries and veins, and the appearance and entity of the modification differ as the retinopathy becomes milder or more severe. In order to develop an automatic procedure for the diagnosis and grading of retinopathy, it is necessary to be able to discriminate ...


Automatic retinal vessel tortuosity measurement

ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2010

Retinal vessel tortuosity measurement is the reliable method used as an opthalmological diagnostic tool. For example, Retinopathy of Prematurity (ROP), this disease can be characterised by the increasing in vascular dilation and tortuosity. This paper proposes a method of how to measure and define tortuous scale using the mean curvature. The tortuosity measurement is calculated base on the radius derive ...


Extraction of Blood Vascular Network for Development of an Automated Diabetic Retinopathy Screening System

2009 International Conference on Computer Technology and Development, 2009

Segmentation of vascular structures of retina for implementation of Clinical diabetic retinopathy decision making systems is presented in this paper. As retinal vascular structure is with thin blood vessels, prediction accuracy is highly dependent upon the segmentation and preprocessing schemes. Complex GSZ shock filters have been used for noise suppression and edge enhancement. Binarization algorithms are used to achieve the ...


Image processing and hierarchical temporal memories for automated retina analysis

2010 Biomedical Sciences and Engineering Conference, 2010

Due to the projected increase in the type 2 diabetes epidemic, there is a critical need for widely available and inexpensive screening for diabetic retinopathy, a preventable secondary disease caused by diabetes that can lead to decreased visual function and even blindness. Currently this type of testing can only be performed manually by ophthalmologists, but a telemedicine network with retina ...


Automated detection of diabetic retinopathy using SVM

2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), 2017

Diabetic retinopathy is a common eye disease in diabetic patients and is the main cause of blindness in the population. Early detection of diabetic retinopathy protects patients from losing their vision. Thus, this paper proposes a computer-assisted diagnosis based on the digital processing of retinal images in order to help people detecting diabetic retinopathy in advance. The main goal is ...


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Educational Resources on Retinopathy

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IEEE.tv Videos

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IEEE-USA E-Books

  • A divide et impera strategy for automatic classification of retinal vessels into arteries and veins

    The first pathologic alterations of the retina are seen in the vessel network. These modifications affect very differently arteries and veins, and the appearance and entity of the modification differ as the retinopathy becomes milder or more severe. In order to develop an automatic procedure for the diagnosis and grading of retinopathy, it is necessary to be able to discriminate arteries from veins. The problem is complicated by the similarity in the descriptive features of these two structures and by the contrast and luminosity variability of the retina. We developed a new algorithm for classifying the vessels, which exploits the peculiarities of retinal images. By applying a divide et impera approach that partitioned a concentric zone around the optic disc into quadrants, we were able to perform a more robust local classification analysis. The results obtained by the proposed technique were compared with those provided by a manual classification on a validation set of 443 vessels and reached an overall classification error of 12%, which reduces to 7% if only the diagnostically important retinal vessels are considered.

  • Automatic retinal vessel tortuosity measurement

    Retinal vessel tortuosity measurement is the reliable method used as an opthalmological diagnostic tool. For example, Retinopathy of Prematurity (ROP), this disease can be characterised by the increasing in vascular dilation and tortuosity. This paper proposes a method of how to measure and define tortuous scale using the mean curvature. The tortuosity measurement is calculated base on the radius derive from chord in each curve of the blood vessel. The result obtains from this method can set the standard of tortuosity grading in the retinal vessel. The advantage of this method is to reduce the error in tortuosity calculation, which cause by the low quality image of the retinal vessel.

  • Extraction of Blood Vascular Network for Development of an Automated Diabetic Retinopathy Screening System

    Segmentation of vascular structures of retina for implementation of Clinical diabetic retinopathy decision making systems is presented in this paper. As retinal vascular structure is with thin blood vessels, prediction accuracy is highly dependent upon the segmentation and preprocessing schemes. Complex GSZ shock filters have been used for noise suppression and edge enhancement. Binarization algorithms are used to achieve the segmentation of vascular structures of the retina. It has been observed that the combination of shock filter and Binarization provides better segmentation results compared to morphological and region growing methods.

  • Image processing and hierarchical temporal memories for automated retina analysis

    Due to the projected increase in the type 2 diabetes epidemic, there is a critical need for widely available and inexpensive screening for diabetic retinopathy, a preventable secondary disease caused by diabetes that can lead to decreased visual function and even blindness. Currently this type of testing can only be performed manually by ophthalmologists, but a telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a more cost effective method of providing broadbased screening. In this paper we report on the method of using Hierarchical Temporal Memories (HTMs), a new type of machine learning technology based on the function of the human neocortex, to locate optic nerves as an alternative method for physiological feature location as a part of the larger telemedicine network scheme. We compare the results from the HTM network on a data set collected from a Memphis, TN clinic to the results from more conventional machine vision techniques. We show that while HTM technology as it is used with this procedure is not as accurate as traditional image analysis and processing methods, it is still reasonably effective and is a promising new technology for machine vision applications such as the diabetic retinopathy telemedicine network.

  • Automated detection of diabetic retinopathy using SVM

    Diabetic retinopathy is a common eye disease in diabetic patients and is the main cause of blindness in the population. Early detection of diabetic retinopathy protects patients from losing their vision. Thus, this paper proposes a computer-assisted diagnosis based on the digital processing of retinal images in order to help people detecting diabetic retinopathy in advance. The main goal is to automatically classify the grade of non- proliferative diabetic retinopathy at any retinal image. For that, an initial image processing stage isolates blood vessels, microaneurysms and hard exudates in order to extract features that can be used by a support vector machine to figure out the retinopathy grade of each retinal image. This proposal has been tested on a database of 400 retinal images labeled according to a 4-grade scale of non-proliferative diabetic retinopathy. As a result, we obtained a maximum sensitivity of 95% and a predictive capacity of 94%. Robustness with respect to changes in the parameters of the algorithm has also been evaluated.

  • Simulation of Eye Disease in Virtual Reality

    It is difficult to understand verbal descriptions of visual phenomenon if one has no such experience. Virtual reality offers a unique opportunity to "experience" diminished vision and the problems it causes in daily life. We have developed an application to simulate age-related macular degeneration, glaucoma, protanopia, and diabetic retinopathy in a familiar setting. The application also includes the introduction of eye anatomy representing both normal and pathologic states. It is designed for patient education, health care practitioner training, and eye care specialist education

  • Survey of Diabetic Retinopathy Screening Methods

    This electronic document iDiabetic retinopathy is an abnormality which involves the small blood vessels that targets the central region like macula. It is a progressive disease and main reason that causes loss in vision. Diabetic retinopathy is a vascular illness of the retina which influences patients with diabetes. This harms the retina of eye and leads to visual impairment if level of diabetes is very high. Diabetic retinopathy has no early signs. In some cases vision will get better or worse during the day. So the importance of automatic assessment of macular enema increased. In this paper we have done a survey on the different techniques used for detection diabetic retinopathy. Diabetic retinopathy is composed of a characteristic group of lesions found in the retina of one having diabetes for several years. Detecting the exudates in early stage can prevent vision loss.

  • Classification of Diabetic Retinopathy Stages using Histogram of Oriented Gradients and Shallow Learning

    This study classifies the stage of Diabetic Retinopathy (DR) into three classes, namely normal, mild Non-Proliferative Diabetic Retinopathy (NPDR), and moderate/severe NPDR class. In general, this research is done to solve that problem arises as a result of similarity of image per stages that cannot be assessed invisible. So, it requires a handling where the image of the retina can be categorized into appropriate categories. Based on the problem, two experimental mechanisms were conducted for each hierarchy, i.e approach computer vision that only focus to process the whole image and the approach taken by the medical using texture feature as marker feature to detect DR. The data are obtained from DiaretDB0 public database. In this research, we present Histogram of Oriented Gradients (HOG) to extract feature. To select the best feature from HOG, we used factor analysis as a feature selection method. This step was done to get good performance in classification step. In our experimental design, we implented shallow learning such as Support Vector Machines learning and Random Forest learning to classify moderate/sever NPDR vs. mild NPDR, mild NPDR vs. Normal, and moderate/severe NPDR vs. Normal. The experimental result shows that our proposed method is able to provide good enough performance in terms of time and accuracy. Our proposed method achieved around 85% accuracy for the binary class classification.

  • Microaneurysm detection in retinal images using a rotating cross-section based model

    Retinal image analysis is currently a very vivid field in biomedical image analysis. One of the most challenging tasks is the reliable automatic detection of microaneurysms (MAs). Computer systems that aid the automatic detection of diabetic retinopathy (DR) greatly rely on MA detection. In this paper, we present a method to construct an MA score map, from which the final MAs can be extracted by simple thresholding for a binary output, or by considering all the regional maxima to obtain probability scores. In contrary to most of the currently available MA detectors, the proposed one does not use any supervised training and classification. However, it is still competitive in the field, with a prominent performance in the detection of MAs close to the vasculature, regarding the state-of-the-art methods. The algorithm has been evaluated in a publicly available online challenge.

  • Automated microaneurysms (MAs) detection in digital colour fundus images using matched filter

    Diabetic retinopathy (DR), one of the most common causes of blindness, is a retinal abnormality caused by high glucose in diabetic patients that leads to micro vascular complications. DR has five levels of severity, i.e. no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR and proliferative diabetic retinopathy (PDR). Microaneurysms (MAs), the first sign of NPDR, can be used as a pre-indicator of DR. However, a manual assessment on digital colour fundus images conducted by ophthalmologists is time consuming. This paper introduces a new algorithm for the automated microaneurysms (MAs) detection in digital colour fundus images using matched filter. Generally, the algorithm consists of four phases, namely green band extraction, MAs and blood vessels isolation, MAs and blood vessels detection, and blood vessels removal. To validate the developed algorithm, the results are compared with their ground truths and annotations using ROI based validation. This algorithm obtains an average sensitivity, specificity, accuracy, and false positive number of 91.0603%, 99.9752%, 99.9752% and 256.44 pixels, respectively. This indicates that the proposed algorithm successfully detects microaneurysms and is able to be implemented in a system for DR mass screening purposes.



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