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\%

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\%

Tools for creating wide-field views of the human retina using Optical Coherence Tomography

Optical Coherence Tomography (OCT) has allowed in-vivo viewing of details of retinal layers like never before. With the development of spectral domain OCT (SD-OCT) details of nearly 2µm axial resolution and higher imaging speed have been reported. Nevertheless, a single volume scan of the retina is typically restricted to 6mm x 6mm in size. Having a larger field of view of the retina will definitely enhance the clinical utility of the OCT. A tool was developed for creating wide-field thickness maps of the retina by combining the use of already available tools like i2k Retina (DualAlign, LLC, Clifton Park, NY) and the thickness maps from Cirrus HD-OCT research browser (Carl Zeiss Meditec, Dublin, California, USA). Normal subjects (n=20) were imaged on Zeiss Cirrus HD-OCT using 512x128 Macular Cube scanning protocol. Sixteen overlapping volumetric images were obtained by moving the internal fixation target around such that the final stitched maps were 12mm x 14mm in size. The thickness maps were corrected for inter-individual differences in axial lengths measured using Zeiss IOL Master and averaged to obtain a normative map. An algorithm was also developed for montaging 3-D volume scans. Using this algorithm two OCT volume scans can be registered and stitched together to obtain a larger volume scan. The algorithm can be described as a two step process involving 3-D phase-correlation and 2-D Pseudo-polar Fourier transform (PPFT). In the first step, 3-D phase-correlation provides translation values in the x, y and z axis. The second step involves applying PPFT on each overlapping pair of B-scans to find rotation in the x-y plane. Subsequent volumes can be stitched to obtain a large field of view. We developed a simple and robust method for creating wide-field views of the retina using existing SD-OCT hardware. As segmentation algorithms improve, this method could be expanded to produce wide-field maps of retinal sub-layers, such as the outer nuclear layer or retinal nerve fiber layer. These wide-field views of the retina may prove useful in evaluating retinal diseases involving the peripheral retina (e.g., retinitis pigmentosa and glaucoma)

An Automatic ROI of The Fundus Photography

The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as darkbackground, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is  helpful in further image processing of posterior areas. We hope that this research will be useful for researchers

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Recognition of License Plates and Optical Nerve Pattern Detection Using Hough Transform

The global technique of detection of the features is Hough transform used in image processing, computer vision and image analysis. The detection of prominent line of the object under consideration is the main purpose of the Hough transform which is carried out by the process of voting. The first part of this work is the use of Hough transform as feature vector, tested on Indian license plate system, having font of UK standard and UK standard 3D, which has ten slots for characters and numbers.So tensub images are obtained.These sub images are fed to Hough transform and Hough peaks to extract the Hough peaks information. First two Hough peaks are taken into account for the recognition purposes. The edge detection along with image rotation is also used prior to the implementation of Hough transform in order to get the edges of the gray scale image. Further, the image rotation angle is varied; the superior results are taken under consideration. The second part of this work makes the use of Hough transform and Hough peaks, for examining the optical nerve patterns of eye. An available database for RIM-one is used to serve the purpose. The optical nerve pattern is unique for every human being and remains almost unchanged throughout the life time. So the purpose is to detect the change in the pattern report the abnormality, to make automatic system so capable that they can replace the experts of that field. For this detection purpose Hough Transform and Hough Peaks are used and the fact that these nerve patterns are unique in every sense is confirmed