The image ray transform

Image feature extraction is a fundamental area of image processing and computer vision. There are many ways that techniques can be created that extract features and particularly novel techniques can be developed by taking influence from the physical world. This thesis presents the Image Ray Transform (IRT), a technique based upon an analogy to light, using the mechanisms that define how light travels through different media and analogy to optical fibres to extract structural features within an image. Through analogising the image as a transparent medium we can use refraction and reflection to cast many rays inside the image and guide them towards features, transforming the image in order to emphasise tubular and circular structures.The power of the transform for structural feature detection is shown empirically in a number of applications, especially through its ability to highlight curvilinear structures. The IRT is used to enhance the accuracy of circle detection through use as a preprocessor, highlighting circles to a greater extent than conventional edge detection methods. The transform is also shown to be well suited to enrolment for ear biometrics, providing a high detection and recognition rate with PCA, comparable to manual enrolment. Vascular features such as those found in medical images are also shown to be emphasised by the transform, and the IRT is used for detection of the vasculature in retinal fundus images.Extensions to the basic image ray transform allow higher level features to be detected. A method is shown for expressing rays in an invariant form to describe the structures of an object and hence the object itself with a bag-of-visual words model. These ray features provide a complementary description of objects to other patch-based descriptors and have been tested on a number of object categorisation databases. Finally a different analysis of rays is provided that can produce information on both bilateral (reflectional) and rotational symmetry within the image, allowing a deeper understanding of image structure. The IRT is a flexible technique, capable of detecting a range of high and low level image features, and open to further use and extension across a range of applications

Identification of Hemorrhages in Iris Using Hybrid Morphological Method

In the field of ophthalmology, hemorrhage is the term used more often because of increasing diabetic patients. It’s a challenge amidst the ophthalmologist to distinguish the hemorrhage from the blood vessels, these lands in various problems. In the past various techniques were employed for the detection of the hemorrhage but they were not so accurate and often encountered misclassification between hemorrhage and blood vessels. Precise detection and classification of hemorrhage and blood vessel is very important in the diagnosis of many problems. This paper depicts a mechanized procedure for recognizing hemorrhages in fundus pictures. The acknowledgment of hemorrhages is one of the critical factors in the early finish of diabetic retinopathy. The algorithm proceeds through several steps such as image enhancement, image subtraction, morphological operations such as image thresholding, image strengthening, image thinning, erosion, morphological closing, image complement to suppress blood vessels and to highlight the hemorrhage

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin

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)

Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Multibiometric System Combining Iris and Retina

Tato diplomová práce se zabývá multibiometrickými systémy, specificky potom biometrickou fúzí. Práce popisuje biometrii oka, tedy rozpoznávání na základě sítnice a duhovky. Stěžejní část tvoří návrh a implementace biometrického systému, který je založený na rozpoznání sítnice a duhovky.This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

Aligning scan acquisition circles in optical coherence tomography images of the retinal nerve fibre layer

Optical coherence tomography (OCT) is widely used in the assessment of retinal nerve fibre layer thickness (RNFLT) in glaucoma. Images are typically acquired with a circular scan around the optic nerve head. Accurate registration of OCT scans is essential for measurement reproducibility and longitudinal examination. This study developed and evaluated a special image registration algorithm to align the location of the OCT scan circles to the vessel features in the retina using probabilistic modelling that was optimised by an expectation-maximization algorithm. Evaluation of the method on 18 patients undergoing large numbers of scans indicated improved data acquisition and better reproducibility of measured RNFLT when scanning circles were closely matched. The proposed method enables clinicians to consider the RNFLT measurement and its scan circle location on the retina in tandem, reducing RNFLT measurement variability and assisting detection of real change of RNFLT in the longitudinal assessment of glaucoma

Face Liveness Detection for Biometric Antispoofing Applications using Color Texture and Distortion Analysis Features

Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach