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Peripapillary atrophy (PPA) is an atrophy of preexisting retina tissue. Because of its association with eye diseases such as myopia and glaucoma, it is important to determine the presence of PPA clinically. Experienced ophthalmologists are able to determine the presence of PPA using visual information from the retinal images. However, it is tedious, time consuming and subjective to examine all images especially in a screening program. This paper presents biologically inspired feature (BIF) for the automatic detection of PPA. BIF mimics the process of cortex for visual perception. In the proposed method, a focal region is segmented from the retinal image and the BIF is extracted. Experimental results show that the proposed BIF based approach achieves an accuracy of more than 90% in detecting PPA, much better than previous methods. It can be used to save the workload of ophthalmologists and thus reduce the diagnosis costs. [ABSTRACT FROM PUBLISHER]

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, measurement of choroidal thickness depends on the manual labeling, which is tedious and subjective of inter-observer differences. In this paper, we propose a fast and accurate algorithm that could measure the choroidal thickness automatically. The lower boundary of the choroid is detected by searching the biggest gradient value above the retinal pigment epithelium (RPE) and the upper boundary is formed by finding the shortest path of the graph formed by valley pixels using dynamic programming. The average of Dice's Coefficient on 10 EDI-OCT images is 94.3%, which shows good consistency of the algorithm with the manual labeling. The processing time for each image is about 2 seconds. [ABSTRACT FROM PUBLISHER]

Glaucoma subtype can be identified according to the configuration of the anterior chamber angle(ACA). In this paper, we present an ACA classification approach based on histograms of oriented gradients at multiple scales. In digital optical coherence tomography (OCT) photographs, our method automatically localizes the ACA, and extracts histograms of oriented gradients (HOG) features from this region to classify the angle as an open angle (OA) or an angle-closure(AC). This proposed method has three major features that differs from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, the ACA is directly classified as OA/AC by using multiscale HOG visual features only, which is different from previous ACA assessment approaches that on clinical features. Third, it demonstrates that visual features with higher dimensions outperform low dimensional clinical features in terms of angle closure classification accuracy. Testing was performed on a large clinical dataset, comprising of 2048 images. The proposed method achieves a 0.835±0.068 AUC value and 75.8% ± 6.4% balanced accuracy at a 85% specificity, which outperforms existing ACA classification approaches based on clinical features. [ABSTRACT FROM PUBLISHER]

We present a regional propagation approach based on retinal structure priors to localize the optic cup in 2D fundus images, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to more descriptive and effective features than those employed by pixel based techniques, without additional computational cost. Second, the proposed approach does not need manually labeled training samples, but uses the structural priors on relative cup and disc positions. Third, a refinement scheme that utilizes local context information is adopted to further improve the accuracy. Tested on the ORIGA-light clinical dataset, which comprises of 325 images from a population-based study, the proposed method achieves a 34.9% non-overlap ratio with manually-labeled ground-truth and a 0.104 absolute cup-to-disc ratio (CDR) error. This level of accuracy is much higher than the state-of-the-art pixel based techniques, with a comparable or even less computational cost. [ABSTRACT FROM PUBLISHER]

Optic cup detection remains a challenging task in retinal image analysis, and is of particular importance for glaucoma evaluation, where disease severity is often assessed by the size of the optic cup. In this paper, we propose spatial heuristic ensembling (SHE), an approach which aims to fuse the advantages of each method based on the specific performance in each defined sector. In this way, we generate an ensembled optic cup which is obtained from the optimal combination of the component methods. We conduct experiments on the ORIGA data set of 650 retinal images and show that the ensemble approach performs better than the individual segmentations, reducing the relative overlap error, and CDR errors by as much as 0.04 CDR units. The results are promising for the continued development of such an approach for improving optic cup segmentation. [ABSTRACT FROM PUBLISHER]

The Intraocular Pressure (IOP) is a key modifiable parameter used for the diagnosis of glaucoma, a degenerative eye disease. Most of the current IOP measurement techniques involve contact with the ocular surface (may introduce patient discomfort) and are unsuitable for continuous IOP monitoring, a process which can possibly raise patient care standards. This paper describes the micro-fabrication of a Capacitive Micro-machined Ultrasonic Transducers (CMUT) array which can potentially improve patient comfort with a non-contact measurement approach and lead to better disease management decisions through continuous IOP levels monitoring. Initial testing of CMUT devices submerged in an oil medium gave an output pressure of 40 kPa at 2.5 MHz. (Some figures in this article are in color only in the electronic version) [ABSTRACT FROM AUTHOR]