57 resources related to Cataracts
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
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 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
Artificial Intelligence, Control and Systems, Cyber-physical Systems, Energy and Environment, Industrial Informatics and Computational Intelligence, Robotics, Network and Communication Technologies, Power Electronics, Signal and Information Processing
No periodicals are currently tagged "Cataracts"
2018 International Seminar on Application for Technology of Information and Communication, 2018
Cataracts are diseases caused by the presence of proteins in the lens that form abnormal and gradually enlarged clumps that will interfere with vision by blocking the light entering through the lens. Identification of cataracts is done by taking the image of the eye with a slit-lamp tool from the front of the eye. Slit-lamp images can provide information about ...
2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017
Eye diseases are burning diseases nowadays. Eye illness identification is one of the basic issues in computer vision. Three such diseases have been considered in this paper, cataract, conjunctivitis, and stye. A cataract occurs once protein builds up within the lens of an eye and makes it cloudy which prompts the decrease in vision. Conjunctivitis or pink eye is a ...
2018 4th International Conference on Universal Village (UV), 2018
Cataracts is a serious eye disease, affecting over 20 million people worldwide. It is the clouding of the lens, which blocks the light to go through the lens and project on the retina . As a result, the nerve cannot transfer the whole image to the brain, leading to blindness. A vast majority of cataracts patients are people who are ...
2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), 2018
The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the most common retinal diseases are Diabetic Retinopathy, Glaucoma, and Cataract. The world is ...
2017 21st International Computer Science and Engineering Conference (ICSEC), 2017
Cataract is a clouding or opacity of the eye's lens that can cause vision problems. It is widely accepted that early detection and treatment can reduce the suffering of cataract patients and prevent visual impairment from turning into blindness. This paper compares studies on the use of ensemble learning algorithms for cataract detection from fundus images. Two independent feature sets ...
Cataracts are diseases caused by the presence of proteins in the lens that form abnormal and gradually enlarged clumps that will interfere with vision by blocking the light entering through the lens. Identification of cataracts is done by taking the image of the eye with a slit-lamp tool from the front of the eye. Slit-lamp images can provide information about the condition of the pupils that can only be analyzed by the doctor manually based on doctor's observation and doctor's experience that can cause different analysis in determining the actual eye condition. Things that are considered by the doctor in analyzing cataracts are the level of opacity in the eyes and the area covered by the turbid. Identification and classification with slit-lamp images can be performed better and more accurately using image processing techniques. Firstly, the grayscale method, median filter method and canny method is used to preprocess the slit-lamp images. Next, the hough circular method is used to automatically segment pupil from slit-lamp images. After the segmentation process, we use pixel scanning to extract mean intensity and uniformity from the pupil image. After the feature extraction process, classification is done by single perceptron based on the extracted feature. This research is expected to help the doctor to do cataracts classification so that the classification process will be easier and more accurate. Based on the test result show that the accuracy of the system is 96.6%.
Eye diseases are burning diseases nowadays. Eye illness identification is one of the basic issues in computer vision. Three such diseases have been considered in this paper, cataract, conjunctivitis, and stye. A cataract occurs once protein builds up within the lens of an eye and makes it cloudy which prompts the decrease in vision. Conjunctivitis or pink eye is a condition where the conjunctiva of the eye inflamed by an infection or by an allergic reaction. And the third sort of illness i.e. stye is an infection that causes a young red., painful lump near the edges of the eyelid. Generally., an eye doctor uses a slit lamp camera to find these sicknesses. But because of lack of specialist eye doctor and slit lamp camera in rural areas are the main problem of the belated in detecting those diseases. In this paper first, the captured eye images collected from different patients and processed for improvement. Then HOG used for detection of the feature vector. Finally recognition of disease done with the assistance of minimum distance classifier. This planned method is economical., computationally quick and price very low. The proposed system result with average accuracy is 96.5 percent in classification.
Cataracts is a serious eye disease, affecting over 20 million people worldwide. It is the clouding of the lens, which blocks the light to go through the lens and project on the retina . As a result, the nerve cannot transfer the whole image to the brain, leading to blindness. A vast majority of cataracts patients are people who are over 50 years old. To classify different areas of cataracts in lens, we use supervised training of convolutional neural network to train 420 images of cataracts on the lens taken from slit-lamps. The experiment can make the future of classifying cataracts more easily and ophthalmologists can apply operations to different categories of cataracts within a shorter time to cure patients with cataracts. For those people in the countryside, even not so experienced doctors can take the photo of lens and use the program to classify cataracts correctly.
The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the most common retinal diseases are Diabetic Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.
Cataract is a clouding or opacity of the eye's lens that can cause vision problems. It is widely accepted that early detection and treatment can reduce the suffering of cataract patients and prevent visual impairment from turning into blindness. This paper compares studies on the use of ensemble learning algorithms for cataract detection from fundus images. Two independent feature sets as texture-based and sketch-based are extracted from each fundus image. Three basic learning models as decision tree (DT), back propagation neural network (BPNN) and sequential minimal optimization (SMO) are built on each feature set. Then, the ensemble learning algorithms of majority voting and stacking method are investigated to combine the base learning models for cataract detection. A real-world data set including fundus image samples with no cataract, mild, moderate, and severe cataract is used for training and testing. Experimental results show that good performance results from the stacking method, with texture-based features giving accuracy of detection at 95.479%.
This article presents an extensive survey of recent studies on mobile applications for ocular diseases detection. Survey results show that smartphone applications are the current niche in healthcare, particularly in the diagnosis of ocular diseases. This environment has led to the development of the proposed framework of a corneal disease screening system using mobile phones to overcome the aforementioned limitations. Typically, such systems are integrated with the Internet of Things and image processing with machine learning techniques to process the anterior segment of photographed images. Thus, they are able to serve as automatic systems to assist optometrists in clinical diagnosis.
Physiological hand tremor severely affect the accuracy of the manipulation of ophthalmology robot. Ophthalmologic operation is performed under microscope, where circular continuous curvilinear capsulorhexis during cataract surgery is a typical operation in ophthalmologic surgical requires high accuracy, so tremor suppression is in demand. We introduced three methods: The Weighted Frequency Fourier Linear Combiner(WFLC), Kalman Filter(KF) and Moving Average Filter(MAF) to clarify the effect on physiological tremor suppression. Haptic device was used as master manipulator to gather data, by draw a circle to roughly simulate the circular continuous curvilinear capsulorhexis during cataract surgery. Previous studies have shown that the frequency of physiological hand tremor range of 8 to 12 Hz. Extend it to 6-14Hz for reliability. The results have be shown that: In 10 trails, The Weighted Frequency Fourier Linear Combiner reduced the RMS amplitude of motion by 74% within 6-14Hz in the task we defined, which best fit the requirement among three methods.
Presently Digital Image Processing based automated diagnosis of medical images is very popular area of research. This technique cuts short the diagnosis time and produces results with high degree of accuracy with least variations in diagnostic opinion. For such applications, basic parameter mapping and evolving image processing techniques play a very crucial role. Automated Cataract detection through digital eye images requires circular pupil extraction from eye image. Conventional Hough transform fails to extract the circular pupil area due to presence of similar ring structure in Iris and in between region. This paper explains the distinct approach in iris localization and pupil extraction from eye images by extraction of edges using fuzzy logic approach resulting into effective edge estimation technique and later use of these inputs to circular Hough Transform for detection of both. The results of proposed approach has been compared on MMU1, UTIRIS, and IIT-Delhi eye biometric databases. Experimental results explain effective iris localization and pupil extraction with greater accuracy and lower computational time. Results show that the proposed algorithm can be effectively applied in automated cataract detection where circular regions: Iris Localization and subsequent pupil extraction are key to the detection methodology.
Cataract is one of the most common geriatric diseases, and surgery is known to be the best treatment. Despite the increasing demand for cataract surgery, the opportunity for novice residents to practice cataract surgery is gradually diminishing as the patient and animal ethics become strict. Therefore, there have been many attempts to overcome the lack of experience by using virtual reality training system. So far, most of the surgical training simulation devices so far focused on visual training, and when they have a haptic sense, they are tethered to a fixed station, which is different from the feeling of moving the actual surgical tool. In this study, we have developed a haptic surgical training tool with sensory substitution and virtual-reality-based cataract surgery simulator. To assess and reproduce the tactile senses during surgery, we prepared viscoelastic lens dummies and measured vibrations in contact and motion required during Continuous Circular Capsulorhexis (CCC). Based on measurement we designed vibration models for haptic sensory substitution and applied them to virtual reality simulator. With complete virtual reality training system, the contact vibration was successfully implemented for virtual contacts to reproduced realistic haptic senses.
According to the World Health Organization (WHO), cataract is the most common cause of vision loss and blindness. In this paper, we propose a scheme to grade cataract into six classifications for precise automatic cataract diagnosis. We extract two kinds of features: texture features by gray-level co-occurrence matrix (GLCM) and high-level features via the pre-trained residual network (ResNet). Then, the two kinds of features are fused in a way of dimension expansion, which texture feature vectors are added to the tail of high-level feature vectors. Next, the fused feature vectors are put into support vector machine (SVM) to train and verify. Our scheme can achieve 91.5% average accuracy on cataract for six classifications. In addition, our proposed scheme outperforms the existing methods for four classifications significantly.
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