A novel automated approach of multi-modality retinal image registration and fusion

Biomedical image registration and fusion are usually scene dependent, and require intensive computational effort. A novel automated approach of feature-based control point detection and area-based registration and fusion of retinal images has been successfully designed and developed. The new algorithm, which is reliable and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The relative study of retinal images enhances the information on the fundus image by superimposing information contained in the angiogram image. Through the thesis research, two new contributions have been made to the biomedical image registration and fusion area. The first contribution is the automatic control point detection at the global direction change pixels using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. The second contribution is the heuristic optimization algorithm that maximizes Mutual-Pixel-Count (MPC) objective function. The initially selected control points are adjusted during the optimization at the sub-pixel level. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. To our knowledge, it is the first time that the MPC concept has been introduced into biomedical image fusion area as the measurement criteria for fusion accuracy. The fusion image is generated based on the current control point coordinates when the iteration stops. The comparative study of the presented automatic registration and fusion scheme against Centerline Control Point Detection Algorithm, Genetic Algorithm, RMSE objective function, and other existing data fusion approaches has shown the advantage of the new approach in terms of accuracy, efficiency, and novelty

Methods for automated analysis of macular OCT data

Optical coherence tomography (OCT) is fast becoming one of the most important modalities for imaging the eye. It provides high resolution, cross-sectional images of the retina in three dimensions, distinctly showing its many layers. These layers are critical for normal eye function, and vision loss may occur when they are altered by disease. Specifically, the thickness of individual layers can change over time, thereby making the ability to accurately measure these thicknesses an important part of learning about how different diseases affect the eye. Since manual segmentation of the layers in OCT data is time consuming and tedious, automated methods are necessary to extract layer thicknesses. While a standard set of tools exist on the scanners to automatically segment the retina, the output is often limited, providing measurements restricted to only a few layers. Analysis of longitudinal data is also limited, with scans from the same subject often processed independently and registered using only a single landmark at the fovea. Quantification of other changes in the retina, including the accumulation of fluid, are also generally unavailable using the built-in software. In this thesis, we present four contributions for automatically processing OCT data, specifically for data acquired from the macular region of the retina. First, we present a layer segmentation algorithm to robustly segment the eight visible layers of the retina. Our approach combines the use of a random forest (RF) classifier, which produces boundary probabilities, with a boundary refinement algorithm to find surfaces maximizing the RF probabilities. Second, we present a pair of methods for processing longitudinal data from individual subjects: one combining registration and motion correction, and one for simultaneously segmenting the layers across all scans. Third, we develop a method for segmentation of microcystic macular edema, which appear as small, fluid-filled, cystoid spaces within the retina. Our approach again uses an RF classifier to produce a robust segmentation. Finally, we present the development of macular flatspace (MFS), a computational domain used to put data from different subjects in a common coordinate system where each layer appears flat, thereby simplifying any automated processing. We present two applications of MFS: inhomogeneity correction to normalize the intensities within each layer, and layer segmentation by adapting and simplifying a graph formulation used previously

Graph Theory and Dynamic Programming Framework for Automated Segmentation of Ophthalmic Imaging Biomarkers

<p>Accurate quantification of anatomical and pathological structures in the eye is crucial for the study and diagnosis of potentially blinding diseases. Earlier and faster detection of ophthalmic imaging biomarkers also leads to optimal treatment and improved vision recovery. While modern optical imaging technologies such as optical coherence tomography (OCT) and adaptive optics (AO) have facilitated in vivo visualization of the eye at the cellular scale, the massive influx of data generated by these systems is often too large to be fully analyzed by ophthalmic experts without extensive time or resources. Furthermore, manual evaluation of images is inherently subjective and prone to human error.</p><p>This dissertation describes the development and validation of a framework called graph theory and dynamic programming (GTDP) to automatically detect and quantify ophthalmic imaging biomarkers. The GTDP framework was validated as an accurate technique for segmenting retinal layers on OCT images. The framework was then extended through the development of the quasi-polar transform to segment closed-contour structures including photoreceptors on AO scanning laser ophthalmoscopy images and retinal pigment epithelial cells on confocal microscopy images. </p><p>The GTDP framework was next applied in a clinical setting with pathologic images that are often lower in quality. Algorithms were developed to delineate morphological structures on OCT indicative of diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME). The AMD algorithm was shown to be robust to poor image quality and was capable of segmenting both drusen and geographic atrophy. To account for the complex manifestations of DME, a novel kernel regression-based classification framework was developed to identify retinal layers and fluid-filled regions as a guide for GTDP segmentation.</p><p>The development of fast and accurate segmentation algorithms based on the GTDP framework has significantly reduced the time and resources necessary to conduct large-scale, multi-center clinical trials. This is one step closer towards the long-term goal of improving vision outcomes for ocular disease patients through personalized therapy.</p>Dissertatio

Incorporating spatial and temporal information for microaneurysm detection in retinal images

The retina of the human eye has the potential to reveal crucial information about several diseases such as diabetes. Several signs such as microaneurysms (MA) manifest themselves as early indicators of Diabetic Retinopathy (DR). Detection of these early signs is important from a clinical perspective in order to suggest appropriate treatment for DR patients. This work aims to improve the detection accuracy of MAs in colour fundus images. While it is expected that multiple images per eye are available in a clinical setup, proposed segmentation algorithms in the literature do not make use of these multiple images. This work introduces a novel MA detection algorithm and a framework for combining spatial and temporal images. A new MA detection method has been proposed which uses a Gaussian matched filter and an ensemble classifier with 70 features for the detection of candidates. The proposed method was evaluated on three public datasets (171 images in total) and has shown improvement in performance for two of the sets when compared to a state-of-the-art method. For lesion-based performance, the proposed method has achieved Retinopathy Online Challenge (ROC) scores of 0.3923, 2109 and 0.1523 in the MESSIDOR, DIARETDB1 and ROC datasets respectively. Based on the ensemble algorithm, a framework for the information combination is developed and consists of image alignment, detecting candidates with likelihood scores, matching candidates from aligned images, and finally fusing the scores from the aligned image pairs. This framework is used to combine information both spatially and temporally. A dataset of 320 images that consists of both spatial and temporal pairs was used for the evaluation. An improvement of performance by 2% is shown after combining spatial information. The framework is applied to temporal image pairs and the results of combining temporal information are analyzed and discussed

TOWARDS A COMPUTATIONAL MODEL OF RETINAL STRUCTURE AND BEHAVIOR

Human vision is our most important sensory system, allowing us to perceive our surroundings. It is an extremely complex process that starts with light entering the eye and ends inside of the brain, with most of its mechanisms still to be explained. When we observe a scene, the optics of the eye focus an image on the retina, where light signals are processed and sent all the way to the visual cortex of the brain, enabling our visual sensation. The progress of retinal research, especially on the topography of photoreceptors, is often tied to the progress of retinal imaging systems. The latest adaptive optics techniques have been essential for the study of the photoreceptors and their spatial characteristics, leading to discoveries that challenge the existing theories on color sensation. The organization of the retina is associated with various perceptive phenomena, some of them are straightforward and strictly related to visual performance like visual acuity or contrast sensitivity, but some of them are more difficult to analyze and test and can be related to the submosaics of the three classes of cone photoreceptors, like how the huge interpersonal differences between the ratio of different cone classes result in negligible differences in color sensation, suggesting the presence of compensation mechanisms in some stage of the visual system. In this dissertation will be discussed and addressed issues regarding the spatial organization of the photoreceptors in the human retina. A computational model has been developed, organized into a modular pipeline of extensible methods each simulating a different stage of visual processing. It does so by creating a model of spatial distribution of cones inside of a retina, then applying descriptive statistics for each photoreceptor to contribute to the creation of a graphical representation, based on a behavioral model that determines the absorption of photoreceptors. These apparent color stimuli are reconstructed in a representation of the observed scene. The model allows the testing of different parameters regulating the photoreceptor's topography, in order to formulate hypothesis on the perceptual differences arising from variations in spatial organization

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions