Fast normalized cross-correlation based retinal recognition

Optic Disc (OD) localization is an important step in retinal image analysis especially for grading Diabetic retinopathy and Glaucoma. This paper describes a method to automatically localize the OD position in retinal fundus images. The method localizes the OD by combining intensity and vascular information. Initially, the vascular structure is extracted using 2 dimensional Gabor filtering followed by a simple thresholding technique. Then the initial OD candidates are selected using intensity information. The candidates’ satisfying area and density criterion are considered for the final stage. In the final stage, the entropy of vascular directions on the final candidates was calculated, and the candidate having maximum and optimum entropy was considered as OD region. The proposed method was evaluated on the two publicly available DRIVE and STARE databases. The method was able to obtain 100% of OD localization accuracy on DRIVE database with 2.2 seconds average computation time.

Retinal vessel segmentation takes a significant part in an automated diabetic retinopathy screening task. However, this can be a challenging job because of the low contrast retinal images and the presences of retinal pathologies. Hence, in this paper, we propose a novel matched filter based on the modified Chebyshev type I function for retinal blood vessels candidates detection. The proposed method is combined with the pre-processing and the post-processing phases to have a complete retinal vessel segmentation scheme. The retinal images from the DRIVE and STARE databases, which are equipped with the ground truths are used to evaluate our proposed method in the segmentation scheme. Using our method, the achieved average levels of sensitivity, specificity, and accuracy are 0.756, 0.973, and 0.954, for the DRIVE database, and 0.731, 0.972, and 0.953, for the STARE database, being better than other presented methods. The high results indicate that our method is reliable to be used in an automated detection tool for diabetic retinopathy.

Fundus images are consistently used for the analysis of numerous pathological syndromes. This paper presents a three step thresholding based method to extract the blood vessels from the retinal images. In the first step a unique combination of principal component analysis (PCA) and contrast limited adaptive histogram equalization (CLAHE) is used to enhance the retinal images. In the second step the blood vessels are extracted with the help of global Otsu thresholding method. Finally in the last step morphological cleaning is applied to remove unwanted frills. The proposed method enables implementation to be easier and takes less computational time. The two publicly available DRIVE and STARE databases are taken for evaluation of the performance of the presented method. It achieves an average accuracy, sensitivity and specificity about 0.956, 0.723 and 0.984 for DRIVE database while 0.954, 0.737 and 0.982 for STARE database respectively.

Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.

In this paper, a new algorithm for retinal vessels segmentation is proposed. The algorithm is based on a multiscale modified Dolph-Chebyshev type I function matched filter. Fundus images from the DRIVE and STARE databases are utilized to evaluate the performance of the proposed algorithm. Several performance indicators, such as specificity, sensitivity, and accuracy are used for performance evaluation. Experimental results show that for the DRIVE database, the results of the proposed algorithm are superior to those produced by all compared algorithms. When tested on pathological images from the STARE database, the proposed algorithm also performs better than all competing methods. This indicates that the proposed algorithm is suitable to be used in automatic retinal diseases diagnosis tools.

In this paper, we have developed a new method of accurate detection of retinal blood vessels based on a deep convolutional neural network (CNN) model. This method plays an important role in the observation of many eye diseases. Retinal Images have many issues that make the process of vessels segmentation very hard. We treat each issue of the retina image with the greatest observation to obtain a well-segmented image. The first step is to apply a pre-processing method based on fuzzy logic and image processing tactics. In a second step, in order to generate the segmented images, we propose a strided encoder-decoder CNN model. This network is trained and optimized using the Dice Loss function that supports the class imbalance problem that is in the database. The proposed model has a U-Net shape, but it is deeper and the pooling layers are replaced with strided convolutional layers in the encoder. This modification allows for a more precise segmentation of vessels and accelerates the training process. The last step is post-processing for removing the noisy pixels as well as the shadow of the optic disc. The performance of the proposed method was evaluated on DRIVE and STARE databases. The proposed method gives a sensitivity of 0.802 and 0.801 respectively on DRIVE and STARE, with an accuracy of 0.959 and 0.961 respectively. We focused on sensitivity and accuracy measurements that represent the accuracy of the model, especially tiny vessels. According to the results, the model outperforms many other proposed methods, especially in the above-mentioned measures.

A wide variety of security applications like computers, laptops, mobile phones, ATM machines, physical access control to property etc. are using biometrics based system for human identification. Today’s Biometrics authentication system can provide highest level of security to the user of the service and the service providers. Today’s uni-modal biometric systems has become mature system and being used for low level to medium level security applications. But, for extremely higher level of security requirements like military base, nuclear plants, research laboratories etc., Finger knuckle image and Retina image based combination can be used, as this combinations provides the highest level of accuracy, low error rates, universality etc. Also the security of Multi-modal biometric templates is very importance as biometric template store vital information of the user. In this research work, Multi-modal Biometric system for higher level security using Finger Knuckle image and Retina image with templates security is developed, experiments are carried out and results are calculated using PolyU database for Finger Knuckle image and DRIVE for Retina images. A Finger Knuckle Image processing starts with Median Filtering, CLAHE method and Canny edge detection. The features extraction is carried using SURF feature method. Once the finger knuckle image template is generated, it is encrypted using RSA algorithm and stored in database. The Retina Image Processing is carried out by applying Green channel followed by Gaussian filtering, CLAHE method is applied on outcome of Gaussian filtering. SURF features set is extracted from retina ROI. Retina image template is generated which is later encrypted using RSA algorithm. The final experiment shows that genuine acceptance rate = 100% is achieved for threshold value 0.35 and False Acceptance Rate is only 1%. The scientific contribution of proposed research work is a multi-biometric system with higher accuracy (GAR is 100%), low error rate (FAR is 1%), better template protection by RSA encryption and efficient hybrid model using finger knuckle image and retina image.

Retinal images play a crucial role in the clinical diagnosis and detection of many eye diseases. However, the retinal image is degraded with noise and suffers from low contrast, which makes it difficult to interpret the image precisely and accurately. So retinal image enhancement becomes a vital step before any further processing. Here, a new approach is suggested for fundus image enhancement, where the image is processed in the spatial domain through an optimal statistical feature‐based transformation function (SFTF). The proposed SFTF has four tunable parameters. An optimal set of these parameters is obtained through a newly suggested adaptive enhanced leader particle swarm optimization technique. The suggested method is investigated with freely accessible DRIVE, STARE, and IDRiD databases. The efficiency of the suggested technique is assessed through different validation indices. The results reveal superior performance compared with other popular methods used for vessel enhancement.

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods.

International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEY

This paper proposes a convolutional neural network architecture for blood vessel segmentation in retinal images. The network structure is designed on 7 layers using MatConvNet (three convolutional layers, two pooling layers, one dropout layer and a Softmax layer). The input data, selected from the DRIVE database, of the neural network is preprocessed in Matlab on Green channel. The retinal image was partitioned in patches of dimension 27 × 27 pixels using a sliding box algorithm. The whole network was trained using a number of 400,000 patches. The CNN was implemented using GPU Programming in MATLAB. The results are promising taking into account speed of processing and the simplicity of the network.

Hypertensive Retinopathy (HR) cause serious damage to human body if not diagnosed early. Therefore, the early diagnosis of this anomaly is very important and complicated process. The most prominent aspect of HR is that the thickness of the blood vessels on the retina change. This change can be calculated with Arteriovenous ratio (AVR). Just before AVR calculation, the veins must be found and extracted on the retina image using some image processing techniques. In this study, the detection of vessels which have a very important place in the diagnosis of HR was performed on GPUs. The retina images taken from the DRIVE database were used as test data and the vessels were successfully detected in a much shorter time and without compromising sensitivity.

Aim: To evaluate a new algorithm to detect the optic disc in retinal fundus images on a number of publicly available datasets. Optic Disc detection is an important first step in many automated algorithms, either to be masked out of future processing or for the use in optic disc related disease such as glaucoma and papilledema.Methods: We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location, with refinement from a combination template matching approach in the found region. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images. All images were acquired at a 45°-50° field of view.Results: The method achieves an accuracy of 100, 98.88, and 99.42% on the DRIVE, DIARETDB1, and Messidor databases respectively.Conclusions: The method performs as well or better than state-of-the-art methods on these datasets. Processing takes an average of 32 seconds (+-1.2) to detect the optic disc, with 26 of those seconds used for the vessel enhancement process. The accuracy over a wide variety of images shows that the method is robust and would be optimal for retinal analysis systems that perform vessel enhancement as part of their processing.

In this paper, we propose a new identification method based on the retinal images. In this algorithm, we use rotation invariant features extracted from the retinal images. Then, we use them for identification. Experimental results on a database, including 60 retina images obtained from 40 subjects of DRIVE database and 20 subjects from STARE database, show the average true identification accuracy rate equal to 99.78 percent for the proposed algorithm.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge lies in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.