Fundus Imaging using Aplanat

Retinal photography requires the use of a complex optical system called a fundus camera, capable of illuminating and imaging simultaneously. Because of restriction of aperture stop(pupil), available fundus imaging system suffer limited field of view. Hence peripheral area of retina remains undetected in traditional way. Also, system being prone to common aberrations always makes them to compromise with quality of image. In this thesis we propose a system that uses an aberration free reflector called Aplanat instead of the conventional lens system for the fundus imaging. So Imaging optics will be based on reflection unlike the convectional system which uses lens system and hence refraction principle which results in very negligible aberrations. Also, small reflector size makes it a hand-held system with minimum complexity and power loss for illumination purpose. Working under thermodynamic limit and high numerical aperture makes it possible to inject maximum light at wide angle inside eye which abolish the necessity of mydriasis. The optical system was designed in Zemax Optical Studio 15.5 in mixed sequential mode abilities by inserting aplanat as non sequential CAD object in sequential system comprised of eye and imaging sensor. CAD object was designed using Solid Edge ST8 using the Cartesian data points created in MATLAB. This solid object then imported to Zemax through CAD import ability. Present system propose 3 phase to image retina completely. A narrow throat aplanat for for some part close to optical axis leaving a small hole at optical axis. A wide throat aplanat to image peripheral area. a normal lens system that will cover the center hole. Exploiting overlapping part in the images from all the systems, stitching can be used to get the final complete image. Conjugate plane of retina found to be a curved surface and inside the aplanat which restricts us from using Zemax tool for the imaging purpose as it can not have of axis multiple sensor at desired location in align with conjugate of retina to sense ray bundle. Also, we loose smoothness and accuracy of reflector surface while im- porting the CAD object. So 3D image reconstruction of retina was performed in tool developed by project partner. All three phases covers almost 2000 wide field of retina which counts for 87% of the retina surface. Exploiting overlapping part in the images from all the systems, stitching can be used to get the final complete image

Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

Digital ocular fundus imaging: a review

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a Ciência e TecnologiaFEDErPrograma COMPET

Advanced retinal imaging: Feature extraction, 2-D registration, and 3-D reconstruction

In this dissertation, we have studied feature extraction and multiple view geometry in the context of retinal imaging. Specifically, this research involves three components, i.e., feature extraction, 2-D registration, and 3-D reconstruction. First, the problem of feature extraction is investigated. Features are significantly important in motion estimation techniques because they are the input to the algorithms. We have proposed a feature extraction algorithm for retinal images. Bifurcations/crossovers are used as features. A modified local entropy thresholding algorithm based on a new definition of co-occurrence matrix is proposed. Then, we consider 2-D retinal image registration which is the problem of the transformation of 2-D/2-D. Both linear and nonlinear models are incorporated to account for motions and distortions. A hybrid registration method has been introduced in order to take advantages of both feature-based and area-based methods have offered along with relevant decision-making criteria. Area-based binary mutual information is proposed or translation estimation. A feature-based hierarchical registration technique, which involves the affine and quadratic transformations, is developed. After that, a 3-D retinal surface reconstruction issue has been addressed. To generate a 3-D scene from 2-D images, a camera projection or transformations of 3-D/2-D techniques have been investigated. We choose an affine camera to characterize for 3-D retinal reconstruction. We introduce a constrained optimization procedure which incorporates a geometrically penalty function and lens distortion into the cost function. The procedure optimizes all of the parameters, camera's parameters, 3-D points, the physical shape of human retina, and lens distortion, simultaneously. Then, a point-based spherical fitting method is introduced. The proposed retinal imaging techniques will pave the path to a comprehensive visual 3-D retinal model for many medical applications

Computational analysis of blood flow and oxygen transport in the retinal arterial network

Pathological changes in retinal microvasculature are known to be associated with systemic diseases such as hypertension and diabetes, and may result in potentially disadvantageous blood flow and impair oxygen distribution. Therefore, in order to improve our understanding of the link between systemic diseases and the retinal circulation, it is necessary to develop an approach to quantitatively determine the hemodynamic and oxygen transport parameters in the retinal vascular circulation. This thesis aims to provide more insights into the detailed hemodynamic features of the retinal arterial tree by means of non-invasive imaging and computational modelling. It covers the following two aspects: i) 3D reconstruction of the retinal arterial tree, and ii) development of an image-based computational model to predict blood flow and oxygen transport in realistic subject-specific retinal arterial trees. The latter forms the main body of the thesis. 3D reconstruction of the retinal arterial tree was performed based on retinal images acquired in vivo with a fundus camera and validated using a simple 3D object. The reproduction procedure was found to be feasible but with limited accuracy. In the proposed 2D computational model, the smaller peripheral vessels indistinguishable from the retinal images were represented by self-similar asymmetric structured trees. The non-Newtonian properties of blood, and nonlinear oxyhemoglobin dissociation in the red blood cells and plasma were considered. The simulation results of the computational model were found in good agreement with in vivo measurements reported in the literature. In order to understand the effect of retinal vascular structure on blood flow and oxygen transport, the computational model was applied to subject-specific geometries for a number of hypertensive and diabetic patients, and comparisons were made with results obtained from healthy retinal arterial networks. Moreover, energy analysis of normal and hypertensive subjects was performed using 3D hypothetical models. Finally, the influence of different viscosity models on flow and oxygen transport in a retinal tree and the advantage of low dimensional models were examined. This study has demonstrated the applicability of the image-based computational modelling to study the hemodynamics and oxygen distribution in the retinal arterial network

Retinal Vessel Segmentation using Tensor Voting

Medical imaging studies generate tremendous amounts of data that are reviewedmanually by physicians every day. Medical image segmentation aims to automate theprocess of extracting (segmenting) “interesting” structures from background structuresin the images, saving physicians time and opening the door to more sophisticatedanalysis such as automatically correlating studies over time. This work focuseson segmenting blood vessels (in particular the retinal vasculature), a task that requiresintegrating both local and global properties of the vasculature to produce goodquality segmentations. We use the Tensor Voting framework as it naturally groupsstructures together based on the consensus of locally voting segments. We investigateseveral ways of encoding the image data as tensors and compare our results quantitativelywith a publically available hand-labeled data set. We demonstrate competitiveperformance versus previously published techniques

Computerised stereoscopic measurement of the human retina

The research described herein is an investigation into the problems of obtaining useful clinical measurements from stereo photographs of the human retina through automation of the stereometric procedure by digital stereo matching and image analysis techniques. Clinical research has indicated a correlation between physical changes to the optic disc topography (the region on the retina where the optic nerve enters the eye) and the advance of eye disease such as hypertension and glaucoma. Stereoscopic photography of the human retina (or fundus, as it is called) and the subsequent measurement of the topography of the optic disc is of great potential clinical value as an aid in observing the pathogenesis of such disease, and to this end, accurate measurements of the various parameters that characterise the changing shape of the optic disc topography must be provided. Following a survey of current clinical methods for stereoscopic measurement of the optic disc, fundus image data acquisition, stereo geometry, limitations of resolution and accuracy, and other relevant physical constraints related to fundus imaging are investigated. A survey of digital stereo matching algorithms is presented and their strengths and weaknesses are explored, specifically as they relate to the suitability of the algorithm for the fundus image data. The selection of an appropriate stereo matching algorithm is discussed, and its application to four test data sets is presented in detail. A mathematical model of two-dimensional image formation is developed together with its corresponding auto-correlation function. In the presense of additive noise, the model is used as a tool for exploring key problems with respect to the stereo matching of fundus images. Specifically, measures for predicting correlation matching error are developed and applied. Such measures are shown to be of use in applications where the results of image correlation cannot be independently verified, and meaningful quantitative error measures are required. The application of these theoretical tools to the fundus image data indicate a systematic way to measure, assess and control cross-correlation error. Conclusions drawn from this research point the way forward for stereo analysis of the optic disc and highlight a number of areas which will require further research. The development of a fully automated system for diagnostic evaluation of the optic disc topography is discussed in the light of the results obtained during this research

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces