Joaquim de Moura

3D Retinal Vessel Tree Segmentation and Reconstruction with OCT Images

Detection and analysis of the arterio-venular tree of the retina is a relevant issue, providing useful information in procedures such as the diagnosis of different pathologies. Classical approaches for vessel extraction make use of 2D acquisition paradigms and, therefore, obtain a limited representation of the vascular structure. This paper proposes a new methodology for the automatic 3D segmentation and reconstruction of the retinal arterio-venular tree in Optical Coherence Tomography (OCT) images. The methodology takes advantage of different image analysis techniques to initially segment the vessel tree and estimate its calibers along it. Then, the corresponding depth for the entire vessel tree is obtained. Finally, with all this information, the method performs the 3D reconstruction of the entire vessel tree.

Enhanced visualization of the retinal vasculature using depth information in OCT

Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.

Automatic Identification of Intraretinal Cystoid Regions in Optical Coherence Tomography.

Optical Coherence Tomography (OCT) is, nowadays, one of the most referred ophthalmological imaging techniques. OCT imaging offers a window to the eye fundus in a non-invasive way, permitting the inspection of the retinal layers in a cross sectional visualization. For that reason, OCT images are frequently used in the analysis of relevant diseases such as hypertension or diabetes. Among other pathological structures, a correct identification of cystoid regions is a crucial task to achieve an adequate clinical analysis and characterization, as in the case of the analysis of the exudative macular disease.

Vessel Tree Extraction and Depth Estimation with OCT Images

The identification of the retinal arterio-venular tree is a relevant issue for its analysis in a large variability of procedures. Classical methodologies employ 2D acquisition strategies that obtain a limited representation of the vascular structure. This paper proposes a new methodology for 2D vessel tree extraction and the corresponding depth estimation using Optical Coherence Tomography (OCT) images. This way, the proposal defines a more complete scenario for an adequate posterior vasculature analysis. The methodology employs different image analysis techniques to initially extract the 2D vessel tree. Then, the method maps these 2D positions in the corresponding histological sections of the OCT images and estimates the corresponding depths along all the vessel tree. To test and validate this proposal, this work employed 196 OCT histological images with the corresponding near infrared reflectance retinographies. The methodology provided promising results, indicating an acceptable accuracy in a complex domain as is the vessel tree identification. It provides a coherent 2D vessel tree extraction with the corresponding depth estimations that constitute a scenario with high potentially useful information for posterior medical analysis and diagnostic processes of many diseases as, for example, hypertension or diabetes.

Detection and Characterization of the Sclera - Evaluation of Eye Gestural Reactions to Auditory Stimuli

Hearing assessment becomes a challenge for the audiologists when there are severe difficulties in the communication with the patient. This methodology is aimed at facilitating the audiological evaluation of the patient when cognitive decline, or other communication disorders, complicate the necessary interaction between patient and audiologist for the proper development of the test. In these cases, the audiologist must focus his attention on the detection of spontaneous and unconscious reactions that tend to occur in the eye region of the patient, expressed in most cases as changes in the gaze direction. In this paper, the tracking of the gaze direction is addressed by the study of the sclera, the white area of the eye. The movement is identified and characterized in order to determine whether or not a positive reaction to the auditory stimuli has occurred, so the hearing of the patients can be correctly assessed.

Automatic Detection of Blood Vessels in Retinal OCT Images

The eye is a non-invasive window where clinicians can observe and study in vivo the retinal vasculature, allowing the early detection of different relevant pathologies. In this paper, we present a complete methodology for the automatic vascular detection in retinal OCT images. To achieve this, we analyse the intensity profiles between representative layers of the retina, layers that are previously segmented. Then, we propose the use of two threshold-based strategies for vessel detection, a fixed and an adaptive approach. Both methods have been tested and validated with 128 OCT images, that include 560 vessels that were labelled by an ophthalmologist. The approaches provided satisfactory results, facilitating the doctors’ work and allowing better analysis and treatment of vascular diseases.

Automatic Detection of Epiretinal Membrane in OCT Images by Means of Local Luminosity Patterns

This work presents a novel approach for automatic detection of the epiretinal membrane in Optical Coherence Tomography (OCT) images. A tool able to detect this pathology is very valued since it can prevent further ocular damage by doing an early detection. This approach is based in the location of the inner limiting membrane (ILM) layers of the retina. Then, the detected locations are classified using a local-feature based vector in order to determine presence of the membrane. Different tests are run and compared to establish the appropriateness of the approach as well as its practical validity.

Optical Coherence Tomography Denoising by Means of a Fourier Butterworth Filter-Based Approach

Optical Coherence Tomography (OCT) is affected by ubiquitous speckle noise that difficult the visualization and analysis of the retinal structures. Any denoising strategy should be able to remove efficiently the noise as well as preserves clinical information contained in the images. This information is crucial to analyses the retinal layer tissue that allows the posterior analysis and recognition of relevant diseases as macular edema or diabetic retinopathy.

Artery/vein Classification of Blood Vessel Tree in Retinal Imaging.

Alterations in the retinal microcirculation are signs of relevant diseases such as hypertension, arteriosclerosis, or diabetes. Specifically, arterial constriction and narrowing were associated with early stages of hypertension. Moreover, retinal vasculature abnormalities may be useful indicators for cerebrovascular and cardiovascular diseases. The Arterio-Venous Ratio (AVR), that measures the relation between arteries and veins, is one of the most referenced ways of quantifying the changes in the retinal vessel tree. Since these alterations affect differently arteries and veins, a precise characterization of both types of vessels is a key issue in the development of automatic diagnosis systems. In this work, we propose a methodology for the automatic vessel classification between arteries and veins in eye fundus images. The proposal was tested and validated with 19 near-infrared reflectance retinographies. The methodology provided satisfactory results, in a complex domain as is the retinal vessel tree identification and classification.

Interactive Three-Dimensional Visualization System of the Vascular Structure in OCT Retinal Images

This paper proposes an automated tool for the 3D visualization of the retinal arterio-venular tree using Optical Coherence Tomography (OCT) images. The methodology takes advantage of different image processing techniques that initially segments the vessel tree and estimates its corresponding calibers. Then, the depths for the entire vessel tree are also calculated. With all this information, the 3D reconstruction of the vessel tree is achieved, interpolating with B-splines all the segments, obtaining a smooth representation that facilitates its inspection. This model allows the visualization and manipulation of the 3D vessel tree by means of graphical affine transformations, including translation, scaling and rotation. Thus, the method offers a complete and comfortable visualization of the 3D real layout of the vasculature that permits to proceed with more reliable diagnostic processes involving the retinal microcirculation analysis.