Retina fundus image mask generation using pseudo parametric modeling technique

ABSTRACT (abstract): The use of vascular intersection as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from Fundus images have been widely reported in literatures. In this work, a new hybrid approach that makes use of three different methods of vascular intersection detection namely Modified Cross-Point Number (MCN), Combine Cross-Points Number (CCN) and Artificial Neural Network (ANN) technique is hereby proposed. Result obtained from the application of this technique to both simulated and experimental shows a very high accuracy and precision value in detecting both bifurcation and cross over points. Thus an improvement in bifurcation and vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopath

Cotton Wool Spots in Eye Fundus Scope

Diabetes mellitus é uma doença com um impacto significativo na saúde pública. Trata-se de uma alteração do metabolismo de hidratos de carbono, gorduras e proteínas que são resultado de uma deficiência ou ausência total de secreção/resistência à insulina por parte das células beta do pâncreas. Existem 3 tipos de diabetes, o denominado tipo 1 em que o doente é dependente de insulina, o tipo 2 em que o doente é dependente de insulina e a diabetes gestacional que aparece durante a fase de gravidez. A retinopatia diabética é uma complicação que pode resultar em cegueira. Se for detetada numa fase inicial, pode ser tratada por cirurgia a laser. No entanto, é dificil deteta-la numa fase inicial, uma vez que progride sem sintomas até ocorrer perda de visão de forma irreversível. Assim, se podermos detetar / encontrar exudados algodonosos no fundo de olho utilizando reconhecimento de imagem, anotação automática, sistemas de apoio à decisão de avaliação do risco, conjugados com uma aplicação móvel que permita a aquisição de imagens de fundo de olho, poderemos detetar mais cedo e tratar, evitando o risco cegueira do paciente. Este projeto tem como objetivo desenvolver uma aplicação smartphone baseada em algoritmos de baixo custo, que podem ser altamente eficientes nas imagens de baixa qualidade provenientes da câmara de um smartphone, que pode ser usada como um sistema de apoio à decisão. Este sistema também pode ser extendido a outras doenças oculares, como uma ferramenta útil para o rastreio de saúde ocular nos países em desenvolvimento, reforçar a proximidade dos programas de rastreio para a população. Os principais objetivos são desenvolver sistema fiável de apoio à decisão, considerando exudados algodonosos, juntamente com pontos vermelhos, em vez do sistema actualmente em uso em Portugal, que considera apenas os pontos vermelhos. O número casos Retinopatia Diabética em todo o mundo justifica o desenvolvimento de um sistema de suporte à decisão automatizado para triagem rápida e de baixo custo da Retinopatia Diabética.Diabetes mellitus é uma doença com um impacto significativo na saúde pública. Trata-se de uma alteração do metabolismo de hidratos de carbono, gorduras e proteínas que são resultado de uma deficiência ou ausência total de secreção/resistência à insulina por parte das células beta do pâncreas. Existem 3 tipos de diabetes, o denominado tipo 1 em que o doente é dependente de insulina, o tipo 2 em que o doente é dependente de insulina e a diabetes gestacional que aparece durante a fase de gravidez. A retinopatia diabética é uma complicação que pode resultar em cegueira. Se for detetada numa fase inicial, pode ser tratada por cirurgia a laser. No entanto, é dificil deteta-la numa fase inicial, uma vez que progride sem sintomas até ocorrer perda de visão de forma irreversível. Assim, se podermos detetar / encontrar exudados algodonosos no fundo de olho utilizando reconhecimento de imagem, anotação automática, sistemas de apoio à decisão de avaliação do risco, conjugados com uma aplicação móvel que permita a aquisição de imagens de fundo de olho, poderemos detetar mais cedo e tratar, evitando o risco cegueira do paciente. Este projeto tem como objetivo desenvolver uma aplicação smartphone baseada em algoritmos de baixo custo, que podem ser altamente eficientes nas imagens de baixa qualidade provenientes da câmara de um smartphone, que pode ser usada como um sistema de apoio à decisão. Este sistema também pode ser extendido a outras doenças oculares, como uma ferramenta útil para o rastreio de saúde ocular nos países em desenvolvimento, reforçar a proximidade dos programas de rastreio para a população. Os principais objetivos são desenvolver sistema fiável de apoio à decisão, considerando exudados algodonosos, juntamente com pontos vermelhos, em vez do sistema actualmente em uso em Portugal, que considera apenas os pontos vermelhos. O número casos Retinopatia Diabética em todo o mundo justifica o desenvolvimento de um sistema de suporte à decisão automatizado para triagem rápida e de baixo custo da Retinopatia Diabética.Diabetes mellitus is a disease with significant impact in public health. It is a complex disorder of carbohydrate, fat and protein metabolism that is a result of a deficiency, or complete lack of insulin secretion by the Beta cells of pancreas, or resistance to Insulin. There are 3 types of diabetes, namely type 1 where the patient is insulin-dependent, type 2 where the patient is non insulin-dependent and gestational diabetes that appears during the pregnancy phase.Retinopathy is a diabetes complication that can result in blindness. If detected in an early stage, it can be treated by laser surgery. However its early detection is frequently missed, since it progresses without symptoms until irreversible vision loss occurs.So if we can detect/find cotton wool spots in eye fundus scope by using image recognition, automatic annotation, decision-support systems for risk assessment, conjugate with a mobile app acquiring eye fundus images, we might detect early and treat avoiding patient blindness risk.This project aims to develop a smartphone-based on low computational-cost algorithms, which can be highly efficient in the lower quality images of the smartphone camera, that can be used as a decision-support system. This system may also be extended to other eye diseases, as an useful tool for eye health screening in developing countries and enhance the proximity of screening programs to the population.The main expected contribution is to develop a good decision-support system, considering cotton wool spots, together with red dots, instead of the actual system in use in Portugal which only considers red dots. The number of Diabetic Retinopathy cases worldwide justifies the development of an automated decision-support system for quick and cost effective screening of Diabetic Retinopathy

Detection of pathologies in retina digital images an empirical mode decomposition approach

Accurate automatic detection of pathologies in retina digital images offers a promising approach in clinicalapplications. This thesis employs the discrete wavelet transform (DWT) and empirical mode decomposition (EMD) to extract six statistical textural features from retina digital images. The statistical features are the mean, standard deviation, smoothness, third moment, uniformity, and entropy. The purpose is to classify normal and abnormal images. Five different pathologies are considered. They are Artery sheath (Coat’s disease), blot hemorrhage, retinal degeneration (circinates), age-related macular degeneration (drusens), and diabetic retinopathy (microaneurysms and exudates). Four classifiers are employed; including support vector machines (SVM), quadratic discriminant analysis (QDA), k-nearest neighbor algorithm (k-NN), and probabilistic neural networks (PNN). For each experiment, ten random folds are generated to perform cross-validation tests. In order to assess the performance of the classifiers, the average and standard deviation of the correct recognition rate, sensitivity and specificity are computed for each simulation. The experimental results highlight two main conclusions. First, they show the outstanding performance of EMD over DWT with all classifiers. Second, they demonstrate the superiority of the SVM classifier over QDA, k-NN, and PNN. Finally, principal component analysis (PCA) was employed to reduce the number of features in hope to improve the accuracy of classifiers. We find that there is no general and significant improvement of the performance, however. In sum, the EMD-SVM system provides a promising approach for the detection of pathologies in digital retina

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation

A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis

A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Empirical mode decomposition-based filter applied to multifocal electroretinograms in multiple sclerosis diagnosis

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients

Intellectual System Diagnostics Glaucoma

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease