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

Glaucoma Detection from Color Fundus Images

Glaucoma is a pathological condition, progressive neurodegeneration of the optic nerve, which causes vision loss. The damage to the optic nerve occurs due to the increase in pressure within the eye. Glaucoma is evaluated by monitoring intra ocular pressure (IOP), visual field and the optic disc appearance (cup-to-disc ratio). Cup-to disc ratio (CDR) is normally a time invariant feature. Therefore, it is one of the most accepted indicator of this disease and the disease progression. In this paper, active contour method is used to find the CDR from the color fundus images to determine pathological process of glaucoma. The method is applied on 25 nos of color fundus images obtained from optic disc organization UK having normal and pathological images. The proposed technique able to categorize all the glaucoma disease images

Retinal Image Analysis: A Review

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features

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

A systematic approach for enhancement of homogeneous background images using structural information

Image enhancement is an indispensable pre-processing step for several image processing applications. Mainly, histogram equalization is one of the widespread techniques used by various researchers to improve the image quality by expanding the pixel values to fill the entire dynamic grayscale. It results in the visual artifact, structural information loss near edges due to the information loss (due to many-to-one mapping), and alteration in average luminance to a higher value. This paper proposes an enhancement algorithm based on structural information for homogeneous background images. The intensities are divided into two segments using the median value to preserve the average luminance. Unlike traditional techniques, this algorithm incorporates the spatial locations in the equalization process instead of the number of intensity values occurrences. The occurrences of each intensity concerning their spatial locations are combined using Rènyi entropy to enumerate a discrete function. An adaptive clipping limit is applied to the discrete function to control the enhancement rate. Then histogram equalization is performed on each segment separately, and the equalized segments are integrated to produce an enhanced image. The algorithm’s effectiveness is validated by evaluating the proposed method on CEED, CSIQ, LOL, and TID2013 databases. Experimental results reveal that the proposed method improves the contrast while preserving structural information, detail information, and average luminance. They are quantified by the high value of contrast improvement index, structural similarity index, and discrete entropy, and low value of average mean brightness error values of the proposed method when compared with the methods available in the literature, including deep learning architectures

Hybrid convolutional neural networks with SVM classifier for classification of skin cancer

Background: The dermatologist widely uses digital dermoscopy for the detection of melanoma. The accurate detection of melanoma by clinicians is subjective and further depends on their experience. Fully automated computer-aided diagnosis systems are necessary to eliminate the inter-operator variability inherent in the personal analysis of dermoscopy images.Objective: Automated skin lesion classification is challenging because of the fine-grained difference in the appearance of these lesions on the skin surface. Deep convolutional neural network (CNN) have shown great separability across many fine-grained object classes.Methods: This article presents two novel hybrid CNN models with an SVM classifier at the output layer for classifying dermoscopy images into either benign or melanoma lesions. The features extracted by the first CNN and second CNN models are concatenated and fed to the SVM classifier for classification. The labels obtained from an expert dermatologist are used as a reference to evaluate the performance of the proposed model.Results: The proposed models displayed better results over the state-of-the-art CNN models on the publicly available ISBI 2016 dataset. The proposed models achieved 88.02% and 87.43% accuracy, which remain higher than the traditional CNN models.Conclusions: The proposed framework could attain considerable improvement in accuracy to classify dermoscopy images

Analysis of various techniques for ECG signal in healthcare, past, present, and future

Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global deaths. Among these, heart attacks and strokes were responsible for 85%, whereas CVDs caused 38% of the premature deaths (under age of 70) affected by non-communicable diseases. The rate of death can be delayed and may be prevented by efficiently analyzing the ECG signals (i.e., captured by a non-invasive method) at the early stage of the disease. QRS complex in ECG provides pivotal information about the heart diseases. Many researchers have analyzed the ECG signal by traditional approach and machine learning methods for identifying the heart disorders. Performance of these techniques depend on accurate detection of different parameters (such as: P-, Q-, R-, S-, T-waveforms, QRS complex duration, R-peak, PR-interval, and RR-interval) from the ECG signals. This review paper provides a detail discussion and comparison of various ECG analysis techniques along with their pros and cons. It summarizes the ECG capturing method, databases available for disease detection & classification, and performance measures used by the researchers. Based on these, a future road map is suggested for real time ECG analysis (for identifying the heart related conditions) captured from the wearable devices and suggested the precautionary steps by the artificial system and experts. This method will help in identifying the co-relation of heart disorders with other body organs (such as: retina and brain parts) by analyzing ECG, fundus image, and magnetic resonance imaging (MRI) of human brain