Academic literature on the topic 'Fundus Image'
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Journal articles on the topic "Fundus Image"
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Wintergerst, Maximilian W. M., Linus G. Jansen, Frank G. Holz, and Robert P. Finger. "A Novel Device for Smartphone-Based Fundus Imaging and Documentation in Clinical Practice: Comparative Image Analysis Study." JMIR mHealth and uHealth 8, no. 7 (July 29, 2020): e17480. http://dx.doi.org/10.2196/17480.
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Background Smartphone-based fundus imaging allows for mobile and inexpensive fundus examination with the potential to revolutionize eye care, particularly in lower-resource settings. However, most smartphone-based fundus imaging adapters convey image quality not comparable to conventional fundus imaging. Objective The purpose of this study was to evaluate a novel smartphone-based fundus imaging device for documentation of a variety of retinal/vitreous pathologies in a patient sample with wide refraction and age ranges. Methods Participants’ eyes were dilated and imaged with the iC2 funduscope (HEINE Optotechnik) using an Apple iPhone 6 in single-image acquisition (image resolution of 2448 × 3264 pixels) or video mode (1248 × 1664 pixels) and a subgroup of participants was also examined by conventional fundus imaging (Zeiss VISUCAM 500). Smartphone-based image quality was compared to conventional fundus imaging in terms of sharpness (focus), reflex artifacts, contrast, and illumination on semiquantitative scales. Results A total of 47 eyes from 32 participants (age: mean 62.3, SD 19.8 years; range 7-93; spherical equivalent: mean –0.78, SD 3.21 D; range: –7.88 to +7.0 D) were included in the study. Mean (SD) visual acuity (logMAR) was 0.48 (0.66; range 0-2.3); 30% (14/47) of the eyes were pseudophakic. Image quality was sufficient in all eyes irrespective of refraction. Images acquired with conventional fundus imaging were sharper and had less reflex artifacts, and there was no significant difference in contrast and illumination (P<.001, P=.03, and P=.10, respectively). When comparing image quality at the posterior pole, the mid periphery, and the far periphery, glare increased as images were acquired from a more peripheral part of the retina. Reflex artifacts were more frequent in pseudophakic eyes. Image acquisition was also possible in children. Documentation of deep optic nerve cups in video mode conveyed a mock 3D impression. Conclusions Image quality of conventional fundus imaging was superior to that of smartphone-based fundus imaging, although this novel smartphone-based fundus imaging device achieved image quality high enough to document various fundus pathologies including only subtle findings. High-quality smartphone-based fundus imaging might represent a mobile alternative for fundus documentation in clinical practice.
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Dai, Peishan, Hanwei Sheng, Jianmei Zhang, Ling Li, Jing Wu, and Min Fan. "Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing." International Journal of Biomedical Imaging 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5075612.
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Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.
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Zhao, Wen Dong, You Dong Zhang, and Chun Xia Jin. "A New Method of Fundus Image Enhancement Based on Rough Set and Wavelet Transform." Applied Mechanics and Materials 397-400 (September 2013): 2205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2205.
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Fundus images are complex images with more details, and on the basis of the inadequate fuzzy enhancement algorithm proposed by Pal et al, this article propose an improved algorithm of rough set for fundus image enhancement. The fundus image will be multi-scale decomposed by wavelet transform firstly, and then the subgraphs are enhanced by using rough set to improve the visual effects; finally the processed sub-images will be reconstructed and generated a new-enhanced image. Compared with the Pal algorithm, the new algorithm not only overcomes its weaknesses that the threshold is set with a fixed value, but also reduces the number of iterations. Experimental results show that the improved enhancement algorithm has a better effect on the fundus image enhancement, and the various details of fundus images can be shown better.
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Hernandez-Matas, Carlos, Xenophon Zabulis, Areti Triantafyllou, Panagiota Anyfanti, Stella Douma, and Antonis A. Argyros. "FIRE: Fundus Image Registration dataset." Modeling and Artificial Intelligence in Ophthalmology 1, no. 4 (July 7, 2017): 16–28. http://dx.doi.org/10.35119/maio.v1i4.42.
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Purpose: Retinal image registration is a useful tool for medical professionals. However, performance evaluation of registration methods has not been consistently assessed in the literature. To address that, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed.Methods: The dataset is comprised by 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap between images and the presence/absence of anatomical differences. Ground truth in the form of corresponding image points and a protocol to evaluate registration performance are provided.Results: The proposed protocol is shown to enable quantitative and comparative evaluation of retinal registration methods under a variety of conditions.Conclusion: This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use.
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Ahn, Sangil, Quang T. M. Pham, Jitae Shin, and Su Jeong Song. "Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability." Electronics 10, no. 6 (March 19, 2021): 726. http://dx.doi.org/10.3390/electronics10060726.
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Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model.
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Firdaus Ahmad Fadzil, Ahmad, Zaaba Ahmad, Noor Elaiza Abd Khalid, and Shafaf Ibrahim. "Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures." International Journal of Engineering & Technology 7, no. 4.33 (December 9, 2018): 110. http://dx.doi.org/10.14419/ijet.v7i4.33.23511.
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Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.
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Prastyo, Pulung Hendro, Amin Siddiq Sumi, and Annis Nuraini. "Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image." JITCE (Journal of Information Technology and Computer Engineering) 4, no. 02 (September 30, 2020): 105–9. http://dx.doi.org/10.25077/jitce.4.02.105-109.2020.
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Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.
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Kaur, Kiranjit, and Priyadarshni. "Retinal Fundus Detection Using Skew Symmetric Matrix." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 29, 2017): 103. http://dx.doi.org/10.23956/ijarcsse.v7i7.107.
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The retina is the light sensitive tissue, lining the back of our eye. Light rays are focused onto the retina through our cornea, pupil and lens. The retina converts the light rays into impulses that travel through optic nerve to our brain, where they are interpreted as the images. The task of manually segmenting fundus from retina images is generally time-consuming and difficult. In most settings, the task is done by marking the fundus regions slice-by-slice, which limits the human rater’s view and generates distorted images. Manual segmentation is also typically done largely based on a single image with intensity enhancement provided by an injected contrast agent. In the current research the fundus is detected and extracted in retinal image. Fundus is distinguished from normal tissues by their image intensity, threshold-based or region growing techniques. The fundus in this approach is detected with the help of geometric features. Skew symmetric matrix is used to avoid any angular orientation. In this approach the accuracy on fundus is quite promising. Accuracy of fundus detection is improved according to the area and the acceptance rate .In this approach ,once the image is loaded, it is filtered and normalized. Then superpixels are generated using linear iterative clustering approach and the features are generated. From the available set of features, some of the features are selected using sequential forward selection approach .Classifier is constructed in order to determine different classes in a test image. Proposed work is two class problem in which algorithm is applied that consists of skew symmetric matrix .Experimental results show substantial improvements in the accuracy and the performance of fundus detection as well as in false acceptance rate and false rejection rate.
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Ward, Nicholas P., Stephen Tomliivson, and Christopher J. Taylor. "Image Analysis of Fundus Photographs." Ophthalmology 96, no. 1 (January 1989): 80–86. http://dx.doi.org/10.1016/s0161-6420(89)32925-3.
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Qureshi, Imran, Jun Ma, and Kashif Shaheed. "A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy." Algorithms 12, no. 1 (January 4, 2019): 14. http://dx.doi.org/10.3390/a12010014.
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Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.
Dissertations / Theses on the topic "Fundus Image"
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Gonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.
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Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.
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Colomer, Granero Adrián. "Fundus image analysis for automatic screening of ophthalmic pathologies." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/99745.
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En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.
En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.
Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745
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Běťák, Ondřej. "Multimodální registrace retinálních snímků z fundus kamery a OCT." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219716.
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V první části se práce se zabývá rešerší metod a principů potřebných při registraci obrazu. Dále pak popisuje zobrazovací systémy očního pozadí jako jsou OCT, fundus kamera a SLO. Druhá část práce je zaměřena na praktickou realizaci programů pro registraci snímků z OCT, SLO a fundus kamery v programovém prostředí Matlab.
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Morales, Martínez Sandra. "Fundus characterization for automatic disease screening through retinal image processing." Doctoral thesis, Editorial Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/53933.
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[EN] The World Health Organization estimates that in 2010 there were 285 million people visually impaired in the world. It is calculated that the 80\% of these cases are preventable or treatable. In addition, aging population and chronic disease increase are two factors that predict a higher number of blindness cases in the future. Hypertension, diabetic retinopathy (DR), age-related macular degeneration (AMD) and glaucoma are the most common pathologies in the current society that provoke retinal damage and can be directly related to blindness and vision loss. The early diagnosis of these diseases allows, through appropriate treatment, to reduce costs generated when they are in advanced states and may become chronic. This fact justifies screening campaigns. However, a screening campaign requires a heavy workload for trained experts in the analysis of anomalous patterns of each disease, which in addition to the increase of population at risk, makes these campaigns economically unfeasible. Therefore, the need of automatic screening system developments is highlighted.
The final goal of this thesis is the implementation of novel methods that allow the analysis and processing of fundus images to implement an automatic screening of four of the most important diseases that affect world population. In particular, the main objective of the thesis is to build up algorithms for the characterization of the retinal structures and the retina background in order to assist in the discrimination between a ``normal" and pathological retina.
Mathematical morphology along with other operators are used for the detection of the retinal vessels and the optic disk. The proposed methods work properly on databases with a large degree of variability. Not only have the main structures been segmented, but significant features have also been extracted from them to be used in a computer aided diagnosis software for hypertensive risk determination. The texture of the retina background is also analyzed in this work by means of local binary patterns with the aim of identifying DR and AMD and avoiding the need of segmentation of the characteristic retinal lesions of each disease. The results are promising above all for AMD diagnosis.[ES] La Organización Mundial de la Salud estima que en 2010 había 285 millones de personas con alguna discapacidad visual en el mundo. Se calcula que el 80\% de estos casos son evitables o tratables. Además, el envejecimiento de la población y el aumento de las enfermedades crónicas son dos factores que hacen prever un número todavía mayor de casos de ceguera en el futuro. La hipertensión, la retinopatía diabética (RD), la degeneración macular asociada a la edad (DMAE) y el glaucoma son las enfermedades más comunes que provocan daños en la retina y, por tanto, están directamente relacionadas con la ceguera y con la pérdida de visión. El diagnóstico de estas enfermedades en estadios tempranos permite, mediante el tratamiento adecuado, reducir los costes que generan en estados ya avanzados y que en la mayoría de los casos acaban convirtiéndose en crónicas, lo que justifica la realización de campañas de cribado. Sin embargo, una campaña de cribado exige una gran carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad, lo que sumado al aumento de la población de riesgo, hace que estas campañas sean inviables económicamente. Por lo tanto, se evidencia la necesidad del desarrollo de sistemas de cribado automáticos. El objetivo final del presente trabajo es la implementación de métodos novedosos de análisis de imágenes de fondo de ojo para usarlos en un sistema de cribado de cuatro de las enfermedades más importantes que afectan a la población actual. En concreto, el objetivo principal de la tesis es el desarrollo de algoritmos para la caracterización de las estructuras y del fondo retiniano, los cuales servirán de ayuda para discriminar una retina ``normal" de otra patológica. Para la detección de los vasos retinianos y del disco óptico, se ha usado morfología matemática además de otros operadores. Se ha demostrado que los métodos propuestos para este fin funcionan adecuadamente en bases de datos con un alto grado de variabilidad. No sólo se han segmentado las principales estructuras retinianas, sino que, además, se han extraído sus características más significativas para determinar el riesgo hipertensivo. En este trabajo, también se han analizado las texturas presentes en el fondo de la retina por medio de la teoría de los patrones binarios locales con el objetivo de identificar la RD y la DMAE a la vez que se evita la necesidad de la segmentación de las lesiones específicas de cada enfermedad. Los resultados son prometedores, sobre todo, para la detección de la DMAE.
[CAT] L'Organització Mundial de la Salut estima que en 2010 havia 285 milions de persones amb alguna discapacitat visual en el món. Es calcula que el 80\% d'aquests casos són evitables o tractables. A més, l'envelliment de la població i l'augment de les malalties cròniques són dos factors que fan preveure un número encara major de casos de ceguera en el futur. La hipertensió, la retinopatia diabètica (RD), la degeneració macular associada a l'edat (DMAE) i el glaucoma són les malalties més comuns que provoquen danys en la retina i, per tant, estan directament relacionades amb la ceguera i amb la pèrdua de visió. El diagnòstic d'aquestes malalties en estadis primerencs permet, per mitjà del tractament adequat, reduir els costos que generen en estats ja avançats i que en la majoria dels casos acaben convertint-se en cròniques, la qual cosa justifica la realització de campanyes de garbellament. No obstant això, una campanya de garbellament exigix una gran càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia, que si es suma a l'augment de la població de risc, fa que aquestes campanyes siguen inviables econòmicament. Per tant, s'evidencia la necessitat del desenrotllament de sistemes de garbellament automàtics. L'objectiu final del present treball és la implementació de mètodes nous d'anàlisi d'imatges de fons d'ull per a usar-los en un sistema de garbellament de quatre de les malalties més importants que afecten la població actual. En concret, l'objectiu principal de la tesi és el desenvolupament d'algoritmes per a la caracterització de les estructures i del fons retinià, els quals serviran d'ajuda per a discriminar una retina ``normal" d'una altra patològica. Per a la detecció dels vasos retinians i del disc òptic, s'ha usat morfologia matemàtica a més d'altres operadors. S'ha demostrat que els mètodes proposats per a aquest fi funcionen adequadament en bases de dades amb un alt grau de variabilitat. No sols s'han segmentat les principals estructures retinianes, sinó que, a més, s'han extret les seues característiques més significatives per a determinar el risc hipertensiu. En aquest treball, també s'han analitzat les textures presents en el fons de la retina per mitjà de la teoria dels patrons binaris locals amb l'objectiu d'identificar la RD i la DMAE al mateix temps que s'evita la necessitat de la segmentació de les lesions específiques de cada malaltia. Els resultats són prometedors, sobretot, per a la detecció de la DMAE.
Morales Martínez, S. (2015). Fundus characterization for automatic disease screening through retinal image processing [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53933
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Svoboda, Ondřej. "Pokročilé metody segmentace cévního řečiště na fotografiích sítnice." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220290.
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Segmentation of vasculature tree is an important step of the process of image processing. There are many methods of automatic blood vessel segmentation. These methods are based on matched filters, pattern recognition or image classification. Use of automatic retinal image processing greatly simplifies and accelerates retinal images diagnosis. The aim of the automatic image segmentation algorithms is thresholding. This work primarily deals with retinal image thresholding. We discuss a few works using local and global image thresholding and supervised image classification to segmentation of blood tree from retinal images. Subsequently is to set of results from two different methods used image classification and discuss effectiveness of the vessel segmentation. Use image classification instead of global thresholding changed statistics of first method on healthy part of HRF. Sensitivity and accuracy decreased to 62,32 %, respectively 94,99 %. Specificity increased to 95,75 %. Second method achieved sensitivity 69.24 %, specificity 98.86% and 95.29 % accuracy. Combining the results of both methods achieved sensitivity up to72.48%, specificity to 98.59% and the accuracy to 95.75%. This confirmed the assumption that the classifier will achieve better results. At the same time, was shown that extend the feature vector combining the results from both methods have increased sensitivity, specificity and accuracy.
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Xu, Xiayu. "Automated delineation and quantitative analysis of blood vessels in retinal fundus image." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3017.
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Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many studies has been done to explore these relationships. However, most of the studies are based on limited data obtained using manual or semi-automated methods due to the lack of automated techniques in the measurement and analysis of retinal vasculature. In this thesis, a fully automated retinal vessel width measurement technique is proposed. This novel method models the accurate vessel boundary delineation problem in two-dimension into an optimal surface segmentation problem in threedimension. Then the optimal surface segmentation problem is transformed into finding a minimum-cost closed set problem in a vertex-weighted geometric graph. The problem is modeled differently for straight vessel and for branch point because of the different conditions in straight vessel and in branch point. Furthermore, many of the retinal image analysis needs the location of the optic disc and fovea as a prerequisite information, for example, in the analysis of the relationship between vessel width and the distance to the optic disc. Hence, a simultaneous optic disc and fovea detection method is presented, which includes a two-step classification of three classes. The major contributions of this thesis include: 1) developing a fully automated vessel width measurement technique for retinal blood vessels, 2) developing a simultaneous optic disc and fovea detection method, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies.
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Pinkava, Marek. "Extrakce krevního řečiště z Fundus snímku lidského oka." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220635.
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This thesis deals with processing of retinal fundus images. Vision is the most important human sense and its injury has very serious consequences for humans. Automatic processing of retinal images increases the efficiency of medical examination and accelerates diagnoses of deseases. Retina exhibits unique characteristics for each person and thus can also be used to identify people. In this task is briefly discussed the structure and properties of each parts of the eye, particularly the retina, and their possible diseases such as diabetic retinopathy, glaucoma and age related macular degeneration. Subsequently, the task describes the representation and characteristics of the digital image. Also is devoted to selected image segmentation methods namely thresholding, edge detection and segmentation techniques based on the matched filter. The outcome of this task is the application in which several segmentation methods are implemented for the blood vessels extraction. For each of these methods it is possible to set the parameters of the segmentation to ensure high quality blood vessels extraction in images of different quality.
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Díaz, Pinto Andrés Yesid. "Machine Learning for Glaucoma Assessment using Fundus Images." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/124351.
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[ES] Las imágenes de fondo de ojo son muy utilizadas por los oftalmólogos para la evaluación de la retina y la detección de glaucoma. Esta patología es la segunda causa de ceguera en el mundo, según estudios de la Organización Mundial de la Salud (OMS).
En esta tesis doctoral, se estudian algoritmos de aprendizaje automático (machine learning) para la evaluación automática del glaucoma usando imágenes de fondo de ojo. En primer lugar, se proponen dos métodos para la segmentación automática. El primer método utiliza la transformación Watershed Estocástica para segmentar la copa óptica y posteriormente medir características clínicas como la relación Copa/Disco y la regla ISNT. El segundo método es una arquitectura U-Net que se usa específicamente para la segmentación del disco óptico y la copa óptica.
A continuación, se presentan sistemas automáticos de evaluación del glaucoma basados en redes neuronales convolucionales (CNN por sus siglas en inglés). En este enfoque se utilizan diferentes modelos entrenados en ImageNet como clasificadores automáticos de glaucoma, usando fine-tuning. Esta nueva técnica permite detectar el glaucoma sin segmentación previa o extracción de características. Además, este enfoque presenta una mejora considerable del rendimiento comparado con otros trabajos del estado del arte.
En tercer lugar, dada la dificultad de obtener grandes cantidades de imágenes etiquetadas (glaucoma/no glaucoma), esta tesis también aborda el problema de la síntesis de imágenes de la retina. En concreto se analizaron dos arquitecturas diferentes para la síntesis de imágenes, las arquitecturas Variational Autoencoder (VAE) y la Generative Adversarial Networks (GAN). Con estas arquitecturas se generaron imágenes sintéticas que se analizaron cualitativa y cuantitativamente, obteniendo un rendimiento similar a otros trabajos en la literatura.
Finalmente, en esta tesis se plantea la utilización de un tipo de GAN (DCGAN) como alternativa a los sistemas automáticos de evaluación del glaucoma presentados anteriormente. Para alcanzar este objetivo se implementó un algoritmo de aprendizaje semi-supervisado.[CAT] Les imatges de fons d'ull són molt utilitzades pels oftalmòlegs per a l'avaluació de la retina i la detecció de glaucoma. Aquesta patologia és la segona causa de ceguesa al món, segons estudis de l'Organització Mundial de la Salut (OMS). En aquesta tesi doctoral, s'estudien algoritmes d'aprenentatge automàtic (machine learning) per a l'avaluació automàtica del glaucoma usant imatges de fons d'ull. En primer lloc, es proposen dos mètodes per a la segmentació automàtica. El primer mètode utilitza la transformació Watershed Estocàstica per segmentar la copa òptica i després mesurar característiques clíniques com la relació Copa / Disc i la regla ISNT. El segon mètode és una arquitectura U-Net que s'usa específicament per a la segmentació del disc òptic i la copa òptica. A continuació, es presenten sistemes automàtics d'avaluació del glaucoma basats en xarxes neuronals convolucionals (CNN per les sigles en anglès). En aquest enfocament s'utilitzen diferents models entrenats en ImageNet com classificadors automàtics de glaucoma, usant fine-tuning. Aquesta nova tècnica permet detectar el glaucoma sense segmentació prèvia o extracció de característiques. A més, aquest enfocament presenta una millora considerable del rendiment comparat amb altres treballs de l'estat de l'art. En tercer lloc, donada la dificultat d'obtenir grans quantitats d'imatges etiquetades (glaucoma / no glaucoma), aquesta tesi també aborda el problema de la síntesi d'imatges de la retina. En concret es van analitzar dues arquitectures diferents per a la síntesi d'imatges, les arquitectures Variational Autoencoder (VAE) i la Generative adversarial Networks (GAN). Amb aquestes arquitectures es van generar imatges sintètiques que es van analitzar qualitativament i quantitativament, obtenint un rendiment similar a altres treballs a la literatura. Finalment, en aquesta tesi es planteja la utilització d'un tipus de GAN (DCGAN) com a alternativa als sistemes automàtics d'avaluació del glaucoma presentats anteriorment. Per assolir aquest objectiu es va implementar un algoritme d'aprenentatge semi-supervisat.
[EN] Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.
The research derived from this doctoral thesis has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolía [GRISOLIA/2015/027].
Díaz Pinto, AY. (2019). Machine Learning for Glaucoma Assessment using Fundus Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124351
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Hu, Qiao. "Automatic construction of arterial and venous vascular trees in fundus images." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/3107.
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The retinal vasculature analysis plays an important role in the diagnosis of ophthalmological diseases, as well as general human disorders that manifest on the retina. The fundus photograph is a 2-D color image modality of the retina and is widely used in modern ophthalmology clinics due to its relatively low cost and its non-invasive access to the retina. However, due to the complexity of the retinal vasculature presented on the image and the large variation of the image quality, no automated method is able to re-construct the retinal vasculature (i.e. construct arteriovenous trees) satisfactorily, thus preventing its analysis on large-scale clinical datasets.
In this thesis, we present a systematic and complete study to automatically construct the retinal vasculature on fundus photographs and apply it to a clinical dataset. First of all, a preliminary study is conducted to detect and classify important landmarks in the retinal vasculature using a machine learning method. The evaluation of this method reveals the difficulty of identifying each landmark as an independent target. Then a novel and more global method is proposed to construct retinal arteriovenous trees (A/V trees). The strategy of the proposed method is to build an over-connected vessel network, and separate it into vascular trees, then classify them into A/V trees. Particularly, by taking advantages of specific properties of the retinal vasculature, global and local information are combined together to recognize landmarks of the vasculature. Instead of recognizing each landmark independently as other methods do, this method considers the relationship between landmarks in a more global manner, thus recognizing them simultaneously and globally. With a special graph design, each landmark is associated with multiple possible configurations and costs, and a near optimal solution is selected by minimizing the costs of landmarks and the global property of the whole vascular network. With each landmark recognized, the A/V trees are easily inferred with a pixel classification method. By doing so, local noise in the images and local errors during pre-processing are corrected to some degree, and small vessels that are difficult to classify locally can also be recognized. The proposed method is compared with another method and the evaluation demonstrates its superiority.
To demonstrate its potential applicability, we apply the proposed method on a cohort study data of HIV-infected patients with treatment. New metrics to analyze retinal vessel width is developed based on the A/V trees built using the proposed method, and it is compared with a conventional metric. Statistical analysis reveals the advantages of the new metric and thus indicates the benefit of the proposed method and its potential application on large datasets.
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Klimeš, Filip. "Zpracování obrazových sekvencí sítnice z fundus kamery." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220975.
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Cílem mé diplomové práce bylo navrhnout metodu analýzy retinálních sekvencí, která bude hodnotit kvalitu jednotlivých snímků. V teoretické části se také zabývám vlastnostmi retinálních sekvencí a způsobem registrace snímků z fundus kamery. V praktické části je implementována metoda hodnocení kvality snímků, která je otestována na reálných retinálních sekvencích a vyhodnocena její úspěšnost. Práce hodnotí i vliv této metody na registraci retinálních snímků.
Books on the topic "Fundus Image"
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Abdollahi, Mohsen. Computer aided processing and analysis of fundus images. Birmingham: University of Birmingham, 1988.
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Burton, Terry. Naming rights: Legacy gifts and corporate money. Hoboken, N.J: Wiley, 2008.
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Liyanage, Sidath E., Fred K. Chen, and James W. Bainbridge. Vitreoretinal surgery. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199672516.003.0005.
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This chapter explores vitreoretinal surgery. It starts off with a detailed examination of retinal anatomy, including a discussion of retinal embryology, and then discusses the physiology of the retina. Next, it outlines the clinical skills of posterior segment history taking and examination. It then discusses the use of diagnostic lenses, which enable visualization of the fundus by neutralizing the optical power of the eye (direct lenses) or increasing the refractive power of the eye to create an inverted real image of the fundus anterior to the eye (indirect lenses). It then continues with a discussion of the practical skills of optical coherence tomography, ultrasonography, and retinal photocoagulation. The chapter also outlines clinical knowledge areas of vitreous disorders, retinal detachment, peripheral retinal abnormalities, macular surgery, submacular surgery, retinal tumours, choroidal tumours, vitreoretinopathies, and posterior segment trauma.
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Ash, Eric. Scanned Image Microscopy (Rank Prize Funds Opto-Electronics Biennial Symposia). Academic Press, 1997.
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Ash, Eric. Scanned Image Microscopy (Rank Prize Funds Opto-Electronics Biennial Symposia). Academic Press, 1997.
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Clift, Ben. The IMF, the UK Policy Debate, and Debt and Deficit Discourse. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198813088.003.0006.
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This chapter charts changing character of the economic ideas informing fiscal policymaking in Britain, and Fund responses to them. Drawing on interviews with the Fund’s UK Missions and UK authorities, it shows how, despite the IMF’s prizing of its non-political, scientific image, its differing views of UK policy space and prioritization became the stuff of a contested politics. The central assumption of the coalition government’s construction of fiscal rectitude was that Britain faced a ‘crisis of debt’, yet the IMF did not share this view. Fund work on fiscal multipliers being higher during recessions, and the adverse effects of fiscal consolidation on growth, all had pointed relevance for UK policy. The coalition government saw little potential for activist fiscal policy in support of growth. In 2013 Blanchard accused the UK authorities of ‘playing with fire’ by pursuing excessively harsh austerity which threatened a prolonged and deep recession.
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Braddick, O. J., and A. C. Sleigh. Physical and Biological Processing of Images: Proceedings of an International Symposium Organised by the Rank Prize Funds, London, England, 27–29 ... Series in Information Sciences ). Springer, 2011.
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Zacek, Natalie A. The Caribbean and West Indies. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199644636.003.0010.
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This chapter examines the development of the Church of England in the West Indian colonies, notably Barbados, Jamaica, and the Leeward Islands of Antigua, Montserrat, Nevis, and St Kitts, in the era of ‘sugar and slavery’ (the mid-seventeenth through to late eighteenth centuries). In many instances, the Church’s mission was undermined by a critical lack of clergy, churches, and funds, and by an apparent lack of interest on the part of the planter elite in either attending or financially supporting religious activities. But it would be inaccurate to consider the Church to have played an insignificant role in these colonial societies; in reality, Anglicanism was a crucial element in the political and social, if not always the spiritual, identity of the islands’ white inhabitants, as it was an integral part of their individual and communal self-image as ‘Englishmen transplanted’.
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Mitrani, Sam. The Eight-Hour Strikes, the Haymarket Bombing, and the Consolidation of the Chicago Police Department. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038068.003.0009.
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This chapter examines how the Eight-hour strikes of 1886 and the Haymarket bombing transformed the Chicago Police Department into a much stronger institution. It begins with a discussion of the Haymarket affair and how it led to massive strikes for the eight-hour workday that began on May 1, 1886. It then considers how the Haymarket bombing changed what it meant to be a member of the Chicago Police Department as well as the relationship between the police, the press, and the city government. It shows that the Haymarket and its aftermath consolidated a positive image of the Chicago Police Department in the eyes of the respectable citizens of the city. This shift facilitated some institutional changes that favored police officers, such as the allocation of funds for a pension, improvements in police buildings, and expansion of the force. But most of all, only citizens willing to risk being identified with the anarchists would criticize the institution itself.
Book chapters on the topic "Fundus Image"
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Wang, Li, and Abhir Bhalerao. "Model Based Segmentation for Retinal Fundus Images." In Image Analysis, 422–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_57.
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Li, Wanyue, Yi He, Wen Kong, Jing Wang, Guohua Deng, Yiwei Chen, and Guohua Shi. "SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image." In Simulation and Synthesis in Medical Imaging, 110–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87592-3_11.
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Ray, Abheek, Ayantika Chakraborty, Dipankar Roy, Barun Sengupta, and Mainak Biswas. "Blood Vessel Extraction from Fundus Image." In Advances in Intelligent Systems and Computing, 259–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1501-5_22.
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Adal, Kedir M., Ronald M. Ensing, Rosalie Couvert, Peter van Etten, Jose P. Martinez, Koenraad A. Vermeer, and L. J. van Vliet. "A Hierarchical Coarse-to-Fine Approach for Fundus Image Registration." In Biomedical Image Registration, 93–102. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08554-8_10.
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Kolar, Radim, Bernhard Hoeher, Jan Odstrcilik, Bernhard Schmauss, and Jiri Jan. "Registration of Image Sequences from Experimental Low-Cost Fundus Camera." In Biomedical Image Registration, 174–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08554-8_18.
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Mueller, Simon, Snezhana Karpova, Maximilian W. M. Wintergerst, Kaushik Murali, Mahesh P. Shanmugam, Robert P. Finger, and Thomas Schultz. "Automated Detection of Diabetic Retinopathy from Smartphone Fundus Videos." In Ophthalmic Medical Image Analysis, 83–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63419-3_9.
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Rekha, R., V. P. Brintha, and P. Anushree. "Heart Disease Prediction Using Retinal Fundus Image." In Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications, 765–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24051-6_71.
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Zhou, Kang, Zaiwang Gu, Annan Li, Jun Cheng, Shenghua Gao, and Jiang Liu. "Fundus Image Quality-Guided Diabetic Retinopathy Grading." In Computational Pathology and Ophthalmic Medical Image Analysis, 245–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00949-6_29.
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Trucco, Emanuele, Andrea Giachetti, Lucia Ballerini, Devanjali Relan, Alessandro Cavinato, and Tom MacGillivray. "Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire." In Biomedical Image Understanding, 91–111. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118715321.ch3.
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Kolar, Radim, and Pavel Tasevsky. "Registration of 3D Retinal Optical Coherence Tomography Data and 2D Fundus Images." In Biomedical Image Registration, 72–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14366-3_7.
Full textConference papers on the topic "Fundus Image"
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Xu, Mengdi, Jun Cheng, Damon Wing Kee Wong, Ching-Yu Cheng, Seang Mei Saw, and Tien Yin Wong. "Automated Tessellated Fundus Detection in Color Fundus Images." In Ophthalmic Medical Image Analysis Third International Workshop. Iowa City, IA: University of Iowa, 2016. http://dx.doi.org/10.17077/omia.1043.
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A, Aruna K., Vrinda S. Anil, Anju Anand, Anagha Jaysankar, Anjali Venugopal, K. L. Nisha, and G. Sreelekha. "Image Mosaicing for Neonatal Fundus Images." In 2021 8th International Conference on Smart Computing and Communications (ICSCC). IEEE, 2021. http://dx.doi.org/10.1109/icscc51209.2021.9528163.
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Binti Sabri, Nur Rafidah, and Haniza Binti Yazid. "Image Enhancement Methods For Fundus Retina Images." In 2018 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2018. http://dx.doi.org/10.1109/scored.2018.8711106.
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Zheng, Jin, Liye Guo, Lihui Peng, Jianqiang Li, Jijiang Yang, and Qingfeng Liang. "Fundus image based cataract classification." In 2014 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2014. http://dx.doi.org/10.1109/ist.2014.6958452.
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Guo, Yong-Xin, Lei Hu, Yan-Chun Li, Qing Jiao, Guang-Yu Zhang, Di Chen, and Dong Cui. "Measurement of fundus vessel diameter based on fundus fluorescence angiography image." In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6512902.
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Ilyasova, Nataly, Nikita Demin, Alexandr Shirokanev, and Rustam Paringer. "Fundus Image Segmentation Using Decision Trees." In 2020 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2020. http://dx.doi.org/10.1109/itnt49337.2020.9253229.
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Aouf, Mohamad, Sultan Almotatiri, Abdulla Bajahzar, and Ghada Kareem. "Glaucoma Detection from Fundus Camera Image." In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014738.
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Yano, F., Y. Suzuki, K. Idogawa, S. P. Ninomija, H. Arai, and Y. Nakamura. "An image analysis of fundus photograph." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94621.
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Liu, J., F. S. Yin, D. W. K. Wong, Z. Zhang, N. M. Tan, C. Y. Cheung, M. Baskaran, T. Aung, and T. Y. Wong. "Automatic glaucoma diagnosis from fundus image." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090916.
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Ithapu, Vamsi K., Armin Fritsche, Ariane Oppelt, Martin Westhofen, and Thomas M. Deserno. "Fundus image registration for vestibularis research." In SPIE Medical Imaging, edited by Nico Karssemeijer and Ronald M. Summers. SPIE, 2010. http://dx.doi.org/10.1117/12.844475.
Full textReports on the topic "Fundus Image"
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Ushizima, Daniela, and Jorge Cuadros. Image analysis of ocular fundus for retinopathy characterization. Office of Scientific and Technical Information (OSTI), February 2010. http://dx.doi.org/10.2172/1004692.
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Payment Systems Report - June of 2020. Banco de la República de Colombia, February 2021. http://dx.doi.org/10.32468/rept-sist-pag.eng.2020.
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With its annual Payment Systems Report, Banco de la República offers a complete overview of the infrastructure of Colombia’s financial market. Each edition of the report has four objectives: 1) to publicize a consolidated account of how the figures for payment infrastructures have evolved with respect to both financial assets and goods and services; 2) to summarize the issues that are being debated internationally and are of interest to the industry that provides payment clearing and settlement services; 3) to offer the public an explanation of the ideas and concepts behind retail-value payment processes and the trends in retail payments within the circuit of individuals and companies; and 4) to familiarize the public, the industry, and all other financial authorities with the methodological progress that has been achieved through applied research to analyze the stability of payment systems. This edition introduces changes that have been made in the structure of the report, which are intended to make it easier and more enjoyable to read. The initial sections in this edition, which is the eleventh, contain an analysis of the statistics on the evolution and performance of financial market infrastructures. These are understood as multilateral systems wherein the participating entities clear, settle and register payments, securities, derivatives and other financial assets. The large-value payment system (CUD) saw less momentum in 2019 than it did the year before, mainly because of a decline in the amount of secondary market operations for government bonds, both in cash and sell/buy-backs, which was offset by an increase in operations with collective investment funds (CIFs) and Banco de la República’s operations to increase the money supply (repos). Consequently, the Central Securities Depository (DCV) registered less activity, due to fewer negotiations on the secondary market for public debt. This trend was also observed in the private debt market, as evidenced by the decline in the average amounts cleared and settled through the Central Securities Depository of Colombia (Deceval) and in the value of operations with financial derivatives cleared and settled through the Central Counterparty of Colombia (CRCC). Section three offers a comprehensive look at the market for retail-value payments; that is, transactions made by individuals and companies. During 2019, electronic transfers increased, and payments made with debit and credit cards continued to trend upward. In contrast, payments by check continued to decline, although the average daily value was almost four times the value of debit and credit card purchases. The same section contains the results of the fourth survey on how the use of retail-value payment instruments (for usual payments) is perceived. Conducted at the end of 2019, the main purpose of the survey was to identify the availability of these payment instruments, the public’s preferences for them, and their acceptance by merchants. It is worth noting that cash continues to be the instrument most used by the population for usual monthly payments (88.1% with respect to the number of payments and 87.4% in value). However, its use in terms of value has declined, having registered 89.6% in the 2017 survey. In turn, the level of acceptance by merchants of payment instruments other than cash is 14.1% for debit cards, 13.4% for credit cards, 8.2% for electronic transfers of funds and 1.8% for checks. The main reason for the use of cash is the absence of point-of-sale terminals at commercial establishments. Considering that the retail-payment market worldwide is influenced by constant innovation in payment services, by the modernization of clearing and settlement systems, and by the efforts of regulators to redefine the payment industry for the future, these trends are addressed in the fourth section of the report. There is an account of how innovations in technology-based financial payment services have developed, and it shows that while this topic is not new, it has evolved, particularly in terms of origin and vocation. One of the boxes that accompanies the fourth section deals with certain payment aspects of open banking and international experience in that regard, which has given the customers of a financial entity sovereignty over their data, allowing them, under transparent and secure conditions, to authorize a third party, other than their financial entity, to request information on their accounts with financial entities, thus enabling the third party to offer various financial services or initiate payments. Innovation also has sparked interest among international organizations, central banks, and research groups concerning the creation of digital currencies. Accordingly, the last box deals with the recent international debate on issuance of central bank digital currencies. In terms of the methodological progress that has been made, it is important to underscore the work that has been done on the role of central counterparties (CCPs) in mitigating liquidity and counterparty risk. The fifth section of the report offers an explanation of a document in which the work of CCPs in financial markets is analyzed and corroborated through an exercise that was built around the Central Counterparty of Colombia (CRCC) in the Colombian market for non-delivery peso-dollar forward exchange transactions, using the methodology of network topology. The results provide empirical support for the different theoretical models developed to study the effect of CCPs on financial markets. Finally, the results of research using artificial intelligence with information from the large-value payment system are presented. Based on the payments made among financial institutions in the large-value payment system, a methodology is used to compare different payment networks, as well as to determine which ones can be considered abnormal. The methodology shows signs that indicate when a network moves away from its historical trend, so it can be studied and monitored. A methodology similar to the one applied to classify images is used to make this comparison, the idea being to extract the main characteristics of the networks and use them as a parameter for comparison. Juan José Echavarría Governor