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[IEEE 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Vilnius, Lithuania (2018.11.8-2018.11.10)] 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Deep Neural Network-based Feature Descriptor for Retinal Image Registration
摘要： Feature description is an important step in image registration workflow. Discriminative power of feature descriptors affects feature matching performance and overall results of image registration. Deep Neural Network-based (DNN) feature descriptors are emerging trend in image registration tasks, often performing equally or better than hand-crafted ones. However, there are no learned local feature descriptors, specifically trained for human retinal image registration. In this paper we propose DNN-based feature descriptor that was trained on retinal image patches and compare it to well-known hand-crafted feature descriptors. Training dataset of image patches was compiled from nine online datasets of eye fundus images. Learned feature descriptor was compared to other descriptors using Fundus Image Registration dataset (FIRE), measuring amount of correctly matched ground truth points (Rank-1 metric) after feature description. We compare the performance of various feature descriptors applied for retinal image feature matching.
关键词： artificial neural networks,biomedical imaging,machine learning,image registration,retinal images,feature descriptors
更新于2025-09-23 15:22:29
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Edge based enhancement of retinal images using an efficient JPEG-compressed domain technique
摘要： With substantial usage of Imaging Technology in the medical field for the diagnosis and treatment of illnesses, a huge volume of medical images are being generated which provide a bigger challenge in terms of storage, transmission and processing. The high resolution medical images thus generated occupy large storage space, and hence they are subjected to compression to make them storage and transmission efficient. Though compression overcomes the issues of storage and transmission to some extent, but the problem of processing compressed images still remains as a challenge. This is because; the usual way of processing the compressed medical images is through the operations of decompression and recompression, which consume lots of computing resources. Therefore, it would be novel, if the compressed medical images are processed and analysed directly in the compressed formats without involving the expensive operations like decompression and recompression. In this direction, the current research paper demonstrates a novel technique of edge based enhancement of retinal images, which is a very critical operation from disease diagnosis perspective, directly in the JPEG compressed domain. The developed algorithm is validated with publicly available retinal datasets of DRIVE and DIARETDB1, and the performance reported is compared with the state-of-the-art techniques in the uncompressed (spatial) domain in terms of both quality of enhancement and computation time.
关键词： compressed domain,Edge enhancement,Discrete Cosine Transform (DCT),JPEG compressed domain,retinal images
更新于2025-09-23 15:22:29
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Deriving probabilistic SVM kernels from flexible statistical mixture models and its application to retinal images classification
摘要： This paper aims to propose a robust hybrid probabilistic learning approach that combines appropriately the advantages of both the generative and discriminative models for the challenging problem of diabetic retinopathy classification in retinal images. We build new probabilistic kernels based on information divergences and Fisher score from the mixture of scaled Dirichlet distributions for support vector machines (SVMs). We also investigate the incorporation of a minimum description length criterion into the learning model to deal with the common problems of determining suitable components and also selecting the best model that describes the dataset. The developed hybrid model is introduced in this paper as an effective SVM kernel able to incorporate prior knowledge about the nature of data involved in the problem at hand and, therefore, permits a good data discrimination. Our approach has been shown to be a better alternative to other methods, which is able to describe the intrinsic nature of datasets and to be of a significant value in a variety of applications involving data classification. We demonstrate the flexibility and the merits of the proposed framework for the problem of diabetic retinopathy detection in eye images.
关键词： Retinal images,SVM,probabilistic kernels,MDL,scaled Dirichlet mixture,generative-discriminative learning
更新于2025-09-23 15:22:29
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Automatic optic disc localization and segmentation in retinal images by a line operator and level sets
摘要： BACKGROUND: Existing methods may fail to locate and segment the optic disc (OD) due to imprecise boundaries, inconsistent image contrast and deceptive edge features in retinal images. OBJECTIVE: To locate the OD and detect the OD boundary accurately. METHODS: The method exploits a multi-stage strategy in the detection procedure. Firstly, OD location candidate regions are identi?ed based on high-intensity feature and vessels convergence property. Secondly, a line operator ?lter for circular brightness feature detection is designed to locate the OD accurately on candidates. Thirdly, an initialized contour is obtained by iterative thresholding and ellipse ?tting based on the detected OD position. Finally, a region-based active contour model in a variational level set formulation and ellipse ?tting are employed to estimate the OD boundary. RESULTS: The proposed methodology achieves an accuracy of 98.67% for OD identi?cation and a mean distance to the closest point of 2 pixels in detecting the OD boundary. CONCLUSION: The results illuminate that the proposed method is effective in the fast, automatic, and accurate localization and boundary detection of the OD. The present work contributes to the more effective evaluation of the OD and realizing automatic screening system for early eye diseases to a large extent.
关键词： optic disc segmentation,level set method,retinal images,Optic disc localization
更新于2025-09-23 15:22:29
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BAT algorithm inspired retinal blood vessel segmentation
摘要： The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.
关键词： digital retinal images,retinal blood vessel segmentation,structured analysis of the retina,feature extraction,BAT algorithm,random forest classifier
更新于2025-09-23 15:21:21
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Automated Optical Flow Based Registration For Adaptive Optics Scanning Laser Ophthalmoscope
摘要： This study presents an automated registration method based on optical flow for an adaptive optics scanning laser ophthalmoscope. The method was designed to align and average images to obtain a higher signal-to-noise ratio image. A correlation based optical flow image registration method, which has large registration degrees of freedom, is adopted as a local registration method. By comparing the images before and after image registration, we show the effectiveness of our method. Furthermore, the advantage of our method, which is the containment of large registration degrees of freedom, is confirmed.
关键词： Retinal images,Image processing,Imaging systems,Optical flow,Active or adaptive optics,Ophthalmology
更新于2025-09-16 10:30:52
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The automated detection of proliferative diabetic retinopathy using dual ensemble classification
摘要： Objective: Diabetic retinopathy (DR) is a retinal vascular disease that is caused by complications of diabetes. Proliferative diabetic retinopathy (PDR) is the advanced stage of the disease which carries a high risk of severe visual impairment. This stage is characterized by the growth of abnormal new vessels. We aim to develop a method for the automated detection of new vessels from retinal images. Methods: This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel maps which each hold vital information. Local morphology, gradient and intensity features are measured using each binary vessel map to produce two separate 21-D feature vectors. Independent classification is performed for each feature vector using an ensemble system of bagged decision trees. These two independent outcomes are then combined to a produce a final decision. Results: Sensitivity and specificity results using a dataset of 60 images are 1.0000 and 0.9500 on a per image basis. Conclusions: The described automated system is capable of detecting the presence of new vessels.
关键词： Retinal images,Ensemble classification,Dual classification,New vessels,Proliferative diabetic retinopathy
更新于2025-09-04 15:30:14