VAMPIRE® fundus image analysis algorithms:Validation and diagnostic relevance in hypertensive cats

OBJECTIVES: To validate a retinal imaging software named VAMPIRE® (Vascular Assay and Measurement Platform for Images of the Retina) in feline patients and test the clinical utility in hypertensive cats. ANIMALS STUDIED: One hundred and five healthy cats were enrolled. They represented the normal dataset used in the validation (group 1). Forty-three hypertensive cats with no noticeable retinal abnormalities were enrolled for the clinical validity of the software (group 2). PROCEDURES: Eleven points (4 veins, 4 arteries, and 3 arterial bifurcations) were measured for each digital image. Repeatability and reproducibility of measurements were assessed using two independent operators. Data were statistically analyzed by the Mann-Whiney and Tukey box plot. Significance was considered when P < 0.05. RESULTS: Two hundred and ten retinal images were analyzed for a total of 2310 measurements. Total mean was 9.1 and 6.1 pixels for veins and arteries, respectively. First, second, and third arteriolar bifurcations angles were 73.6°, 76.9°, and 85.4°, respectively. A comparison between groups 1 and 2 showed a statistically significant reduction in arteriolar diameter (mean 3.3 pixels) and branch angle (55°, 47.8° and 59.9°) associated with increasing vein diameter (mean 24.15 pixels). CONCLUSIONS: Current image analysis techniques used in human medicine were investigated in terms of extending their use to veterinary medicine. The VAMPIRE® algorithm proved useful for an objective diagnosis of retinal vasculature changes secondary to systemic hypertension in cats, and could be an additional diagnostic test for feline systemic hypertension

Assessment of retinal vascular geometry in normal and diabetic subjects

M.D.Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and the leading cause of blindness in persons from age 20 to 74. The relative risk of blindness in persons with diabetes has been reported to be 5.2 times the risk of those without diabetes. The fundus abnormalities described in diabetic retinopathy result from structural damage to the microvasculature wall with subsequent leakage or as a result of retinal ischaemia with secondary overproduction of vascular growth factors. Several clinical and screening classifications schemes have been developed to categorize and quantify the severity of each of the retinopathic features based on the degree of retina involvement. The ultimate goals of these classification schemes have been to provide a system by which the natural history of the disease and the risk of progression of retinopathy and visual loss can be identified and the subsequent response to interventions can be evaluated to improve patient care. The present strategies for dealing with diabetic retinopathy address retinopathy that is already established. However, recent studies - supported by computer based imaging analysis – have focused on changes in retinal vascular caliber and demonstrated various associations with increased risk of diabetes and predicted the onset of microvascular retinal complications. This suggests that other structural and geometrical parameters might also be utilised, which can provide more information regarding the retinal vascular network. Few studies have reported different changes in retinal vascular geometry with age, systemic hypertension and peripheral vascular diseases. The objective of this thesis is to analyse the retinal vascular geometrical features in normal subjects and evaluate its role in diabetic subjects with different stages of diabetic retinopathy. For this purpose, a semi-manual vascular analysis technique is designed to measure and analyse the different retinal vascular geometrical parameters and ratios. The developed technique performance and precision is compared to other available manual and semi-manual vascular analysis techniques.The various sources of variability in retinal geometrical measurements are then evaluated, including observer’s measurement errors, variations in image capture, and potential short term changes in the subjects’ vascular geometrical features. The second step of this work is to perform a detailed analysis of the retinal vascular geometry in normal subjects, including the topographic distribution of different geometrical measurements across the fundus, the effect of different demographic and clinical factors, and the stability of measurements between both eyes. The next step evaluates the retinal vascular geometry in diabetic subjects with different grades of diabetic retinopathy to determine any changes of geometrical features with advancement of retinopathic stages. The results demonstrate significant associations of changes in vascular structural and geometrical features with increased stages of diabetic retinopathy. Finally, the predictive value of retinal vascular geometry analysis and its practical role on the individual level is analysed for a sample of subjects who progressed from no retinopathy to proliferative retinopathy as compared to a sample of subjects with no sign of progression. The preliminary results suggest that geometrical changes trend can be detected on the individual level with progression of diabetic retinopathy and those differences can be noted between progressors and non-progressors at baseline. In conclusion, this thesis describes novel retinal vascular geometrical markers indicative of establishment of advancing diabetic retinopathy, together with a potential predictive role in determining risk of future progression to proliferative retinopathy

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Computer analysis for registration and change detection of retinal images

The current system of retinal screening is manual; It requires repetitive examination of a large number of retinal images by professional optometrists who try to identify the presence of abnormalities. As a result of the manual and repetitive nature of such examination, there is a possibility for error in diagnosis, in particular in the case when the progression of disease is slight. As the sight is an extremely important sense, any tools which can improve the probability of detecting disease could be considered beneficial. Moreover, the early detection of ophthalmic anomalies can prevent the impairment or loss of vision. The study reported in this Thesis investigates computer vision and image processing techniques to analyse retinal images automatically, in particular for diabetic retinopathy disease which causes blindness. This analysis aims to automate registration to detect differences between a pair of images taken at different times. These differences could be the result of disease progression or, occasionally, simply the presence of artefacts. The resulting methods from this study, will be therefore used to build a software tool to aid the diagnosis process undertaken by ophthalmologists. The research also presents a number of algorithms for the enhancement and visualisation of information present within the retinal images, which under normal situations would be invisible to the viewer; For instance, in the case of slight disease progression or in the case of similar levels of contrast between images, making it difficult for the human eye to see or to distinguish any variations. This study also presents a number of developed methods for computer analysis of retinal images. These methods include a colour distance measurement algorithm, detection of bifurcations and their cross points in retina, image registration, and change detection. The overall analysis in this study can be classified to four stages: image enhancement, landmarks detection, registration, and change detection. The study has showed that the methods developed can achieve automatic, efficient, accurate, and robust implementation

Windowed Eigen-Decomposition Algorithm for Motion Artifact Reduction in Optical Coherence Tomography-Based Angiography

Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage