Anterior Chamber Angle Assessment System

In this paper, we propose an automatic anterior chamber angle assessment system for Anterior Segment Optical Coherence Tomography (AS-OCT). In our system, the automatic segmentation method is used to segment the clinical structures, which are then used to recover standard clinical ACA measurements. Our measurements can not only support clinical assessments, but also be utilized as features for detecting anterior angle closure in automatic glaucoma diagnosis

Motion Correction in Optical Coherence Tomography for Multi-modality Retinal Image Registration

Optical coherence tomography (OCT) is a recently developed non-invasive imaging modality, which is often used in ophthalmology. Because of the sequential scanning in form of A-scans, OCT suffers from the inevitable eye movement. This often leads to mis-alignment especially among consecutive B-scans, which affects the analysis and processing of the data such as the registration of the OCT en face image to color fundus image. In this paper, we propose a novel method to correct the mis-alignment among consecutive B-scans to improve the accuracy in multi-modality retinal image registration. In the method, we propose to compute decorrelation from overlapping B-scans and to detect the eye movement. Then, the B-scans with eye movement will be re-aligned to its precedent scans while the rest of B-scans without eye movement are untouched. Our experiments results show that the proposed method improves the accuracy and success rate in the registration to color fundus images

Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

Retinal image quality assessment (IQA) algorithms use different hand crafted features without considering the important role of the human visual system (HVS). We solve the IQA problem using the principles behind the working of the HVS. Unsupervised information from local saliency maps and supervised information from trained convolutional neural networks (CNNs) are combined to make a final decision on image quality. A novel algorithm is proposed that calculates saliency values for every image pixel at multiple scales to capture global and local image information. This extracts generalized image information in an unsupervised manner while CNNs provide a principled approach to feature learning without the need to define hand-crafted features. The individual classification decisions are fused by weighting them according to their confidence scores. Experimental results on real datasets demonstrate the superior performance of our proposed algorithm over competing methods

Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. The presence of drusen is the hallmark of early/intermediate AMD, and their sudden regression is strongly associated with the onset of late AMD. In this work we propose a predictive model of drusen regression using optical coherence tomography (OCT) based features. First, a series of automated image analysis steps are applied to segment and characterize individual drusen and their development. Second, from a set of quantitative features, a random forest classifiser is employed to predict the occurrence of individual drusen regression within the following 12 months. The predictive model is trained and evaluated on a longitudinal OCT dataset of 44 eyes from 26 patients using leave-one-patient-out cross-validation. The model achieved an area under the ROC curve of 0.81, with a sensitivity of 0.74 and a specificity of 0.73. The presence of hyperreflective foci and mean drusen signal intensity were found to be the two most important features for the prediction. This preliminary study shows that predicting drusen regression is feasible and is a promising step toward identification of imaging biomarkers of incoming regression

Vessel Extraction for AS-OCT Angiography

In this work, we propose a filter-based vessel segmentation method for Anterior Segment Optical Coherence Tomography Angiography image. In our method, the bandpass filter is utilized to suppress the horizontal noise lines caused by eye movement, while the curvedsupport Gaussian filter is utilized to enhance the vessel and generate the probability map

Artefacts Removal from Optical Coherence Tomography Angiography

This paper presents a new approach for artefacts removal from optical coherence tomography angiography (OCTA). The artefacts mainly arise as a result of distortion due to eye movements during OCT scanning process. These distortions manifest themselves as visible motion artefacts when doctors review the enface image of OCTA data. To remove these artefacts, firstly we perform motion registration for the captured OCT volume data and subsequently perform motion correction to obtain the registered OCT data. Next, we compute the OCTA from the registered OCT data using an enhanced correlation mapping technique. Thereafter, we compute the enface image from the OCTA data. In the next step, we attempt to locate regions where there is misalignment in the OCT frames of the various B-scans. Finally, we attempt to restore the regions where correct data is postulated to be absent. Our experimental results demonstrate the effectiveness of our proposed approach

Optic Cup Segmentation Using Large Pixel Patch Based CNNs

Optic cup(OC) segmentation on color fundus image is essential for the calculation of cup-to-disk ratio and fundus morphological analysis, which are very important references in the diagnosis of glaucoma. In this paper we proposed an OC segmentation method using convolutional neural networks(CNNs) to learn from big size patch belong to each pixel. The segmentation result is achieved by classification of each pixel patch and postprocessing. With large pixel patch, the network could learn more global information around each pixel and make a better judgement during classification. We tested this method on public dataset Drishti-GS and achieved average F-Score of 93.73% and average overlapping error of 12.25%, which is better than state-of-the-art algorithms. This method could be used for fundus morphological analysis, and could also be employed to other medical image segmentation works which the boundary of the target area is fuzzy

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Accurate segmentation of optic cup and disc in retinal fundus images is required to derive the cup-to-disc ratio (CDR) parameter which is the main indicator for Glaucoma assessment. In this paper, we propose a coupled regression method for accurate segmentation of optic cup and disc in retinal colour fundus image. The proposed coupled regression framework consists of a parameter regressor which directly predicts CDR from a given image, as well as an ensemble shape regressor which iteratively estimates the OD-OC boundary by taking into account the CDR estimated by the parameter regressor. The parameter regressor and the shape regressor are then coupled together within a feedback loop so that estimation of one reinforces the other. Both parameter regressor and the ensemble shape regressor are modeled using Boosted Regression Trees. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrates high segmentation accuracy. A comparative study shows that our proposed method outperforms state of the art methods for cup segmentation