A Review: Person Identification using Retinal Fundus Images

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100\% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90\%

Diagnosis of Retinitis Pigmentosa from Retinal Images

Retinitis pigmentosa is a genetic disorder that results in nyctalopia and its progression leads to complete loss of vision. The analysis and the study of retinal images are necessary, so as to help ophthalmologist in early detection of the retinitis pigmentosa. In this paper fundus images and Optical Coherence Tomography images are comprehensively analyzed, so as to obtain the various morphological features that characterize the retinitis pigmentosa. Pigment Deposits, important trait of RP is investigated. Degree of darkness and entropy are the features used for analysis of PD. The darkness and entropy of the PD is compared with the different regions of the fundus image which is used to detect the pigments in the retinal image. Also the performance of the proposed algorithm is evaluated by using various performance metrics. The performance metrics are calculated for all 120 images of RIPS dataset. The performance metrics such as sensitivity, sensibility, specificity, accuracy, F-score, equal error rate, conformity coefficient, Jaccard's coefficient, dice coefficient, universal quality index were calculated as 0.72, 0.96, 0.97, 0.62, 0.12, 0.09, 0.59, 0.45 and 0.62, respectively

Detection of glaucoma from fundus image using pre-trained Densenet201 model

33-39In recent years, the performance of deep learning algorithms for image recognition has improved tremendously. The inherent ability of a convolutional neural network has made the task of classifying glaucoma and normal fundus images more appropriately. Transferring the weights from the pre-trained model resulted in faster and easier training than training the network from scratch. In this paper, a dense convolutional neural network (Densenet201) has been utilized to extract the relevant features for classification. Training with 80% of the images and testing with 20% of the images has been performed. The performance metrics obtained by various classifiers such as softmax, support vector machine (SVM), knearest neighbor (KNN), and Naive Bayes (NB) have been compared. Experimental results have shown that the softmax classifier outperformed the other classifiers with 96.48% accuracy, 98.88% sensitivity, 92.1% specificity, 95.82% precision, and 97.28% F1-score, with DRISHTI-GS1 database. An increase in the classification accuracy of about 1% has been achieved with enhanced fundus images

Renyi entropy based Bi-histogram equalization for contrast enhancement of MRI brain images

5-11The quality of the MRI brain images is dependent on the sensor. It is essential to have a pre-processing technique to meet the finest quality at the sensor’s cost. A pre-processing algorithm has been proposed in this paper to enhance the low contrast MRI brain images. The input image’s histogram has been divided into two sub histograms using its median value to uphold the input image’s mean brightness. After calculating the Renyi entropy from the sub histogram, histogram clipping has been done to regulate the enhancement rate. The clipping limit has been selected automatically from the minimum value of the mean, median of the distribution function, and itself. Additionally, the proposed algorithm has incorporated the Discrete Cosine Transform (DCT) to improve the enhancement. Experimental results have shown that the proposed algorithm enhances the input image and maintains the mean brightness

Detection of glaucoma from fundus image using pre-trained Densenet201 model

In recent years, the performance of deep learning algorithms for image recognition has improved tremendously. Theinherent ability of a convolutional neural network has made the task of classifying glaucoma and normal fundus imagesmore appropriately. Transferring the weights from the pre-trained model resulted in faster and easier training than trainingthe network from scratch. In this paper, a dense convolutional neural network (Densenet201) has been utilized to extract therelevant features for classification. Training with 80% of the images and testing with 20% of the images has beenperformed. The performance metrics obtained by various classifiers such as softmax, support vector machine (SVM), knearestneighbor (KNN), and Naive Bayes (NB) have been compared. Experimental results have shown that the softmaxclassifier outperformed the other classifiers with 96.48% accuracy, 98.88% sensitivity, 92.1% specificity, 95.82% precision,and 97.28% F1-score, with DRISHTI-GS1 database. An increase in the classification accuracy of about 1% has beenachieved with enhanced fundus images