1,229 research outputs found

    UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

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    One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.Â

    PREMATURE INFANT BLOOD VESSEL SEGMENTATION OF RETINAL IMAGES BASED ON HYBRID METHOD FOR THE DETERMINATION OF TORTUOSITY

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    For the retinal blood vessels segmentation, we used a method, which is based on the morphological operations. The output of this process is extracted retinal binary image, where is situated main blood vessels. In this paper is used dataset of images (2800 images) from device RetCam3. Before applying the image processing, it was selected 30 images with diagnosed pre-plus diseases, and it is divided into two groups with low contrast and good contrast images. In the next part of the analysis, it was analyzing and showing blood vessels with tortuosity. Tortuosity is a symptom of ROP (retinopathy of prematurity). The clinical physicians evaluate tortuosity by visual comparison of the retinal images images. For this reason, it was suggested model which can automatically indicate the tortuosity of the retinal blood vessels by setting a threshold of the blood vessels curvature

    A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

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    Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i4.625

    Computerized Approaches for Retinal Microaneurysm Detection

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    The number of diabetic patients throughout the world is increasing with a very high rate. The patients suffering from long term diabetes have a very high risk of generating retinal disorder called Diabetic Retinopathy(DR). The disease is a complication of diabetes and may results in irreversible blindness to the patient. Early diagnosis and routine checkups by expert ophthalmologist possibly prevent the vision loss. But the number of people to be screen exceeds the number of experts, especially in rural areas. Thus the computerized screening systems are needed which will accurately screen the large amount of population and identify healthy and diseased people. Thus the workload on experts is reduced significantly. Microaneurysms(MA) are first recognizable signs of DR. Thus early detection of DR requires accurate detection of Microaneurysms. Computerized diagnosis insures reliable and accurate detection of MA's. The paper overviews the approaches for computerized detection of retinal Microaneurysms

    Robust Retinal Vessel Segmentation using ELM and SVM Classifier

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    The diagnosis of retinal blood vessels is of much clinical importance, as they are generally examined to evaluate and monitor both the ophthalmological diseases and the non-retinal diseases. The vascular nature of retinal is very complex and the manual segmentation process is tedious. It requires more time and skill. In this paper, a novel supervised approach using Extreme Learning Machine (ELM) classifier and Support Vector Machine (SVM) classifier is proposed to segment the retinal blood vessel. This approach calculates 7-D feature vector comprises of green channel intensity, Median-Local Binary Pattern (M-LBP), Stroke Width Transform (SWT) response, Weber�s Local Descriptor (WLD) measure, Frangi�s vesselness measure, Laplacian Of Gaussian (LOG) filter response and morphological bottom-hat transform. This 7-D vector is given as input to the ELM classifier to classify each pixel as vessel or non-vessel. The primary vessel map from the ELM classifier is combined with the ridges detected from the enhanced bottom-hat transformed image. Then the high-level features computed from the combined image are used for final classification using SVM. The performance of this technique was evaluated on the publically available databases like DRIVE, STARE and CHASE-DB1. The result demonstrates that the proposed approach is very fast and achieves high accuracy about 96.1% , 94.4% and 94.5% for DRIVE, STARE and CHASE-DB1 respectively

    Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

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    Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification

    Fundus Image Classification Using Two Dimensional Linear Discriminant Analysis and Support Vector Machine

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    It is constructed in this study a classification system of diabetic retinopathy fundus image. The system consists of two phases: training and testing. Each stage consists of preprocessing, segmentation, feature extraction and classification. The tested image comes from the MESSIDOR dataset which has a total of 100 images. The number of classes to be classified consists of four classes with each class consists of 25 images. The classes are normal, mild, moderate and severe of Diabetic retinopathy. In this study, the level of preprocessing uses grayscales green channel, Wavelet Haar, Gaussian filter and Contrast Limited Adaptive Histogram Equalization. The level of segmentation uses masking as a process of doing the subtracting operation of between the original image and the masking image. The purpose of the masking is to split between the object and the background. The feature extraction uses Two Dimensional Linear Discriminant Analysis (2DLDA). The classification uses Support Vector Machine (SVM). The test results of some scenarios show that the highest percentage of accuration of the system is up to 90%

    Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

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    The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus
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