Local patch reconstruction framework for optic cup localization in glaucoma detection.

Optic cup localization/segmentation has attracted much attention from medical imaging researchers, since it is the primary image component clinically used for identifying glaucoma, which is a leading cause of blindness. In this work, we present an optic cup localization framework based on local patch reconstruction, motivated by the great success achieved by reconstruction approaches in many computer vision applications recently. Two types of local patches, i.

Robust multi-scale superpixel classification for optic cup localization.

This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution.

Automatic glaucoma diagnosis through medical imaging informatics.

Background: Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease.

Superpixel classification based optic disc and optic cup segmentation for glaucoma screening.

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening.

Automatic localization of retinal landmarks.

Retinal landmark detection is a key step in retinal screening and computer-aided diagnosis for different types of eye diseases, such as glaucomma, age-related macular degeneration(AMD) and diabetic retinopathy. In this paper, we propose a semantic image transformation(SIT) approach for retinal representation and automatic landmark detection. The proposed SIT characterizes the local statistics of a fundus image and boosts the intrinsic retinal structures, such as optic disc(OD), macula.

Automatic detection of the macula in retinal fundus images using seeded mode tracking approach.

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images.

Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype identification.

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).

Sector-based optic cup segmentation with intensity and blood vessel priors.

The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement.

Efficient optic cup localization using regional propagation based on retinal structure priors.

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.

An ensembling approach for optic cup detection based on spatial heuristic analysis in retinal fundus images.

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.

Peripapillary atrophy detection by sparse biologically inspired feature manifold.

Peripapillary atrophy (PPA) is an atrophy of pre-existing retina tissue. Because of its association with eye diseases such as myopia and glaucoma, PPA is an important indicator for diagnosis of these diseases. Experienced ophthalmologists are able to determine the presence of PPA using visual information from the retinal images.

Focal edge association to glaucoma diagnosis.

Glaucoma is an optic nerve disease resulting in the loss of vision. There are two common types of glaucoma: open angle glaucoma and angle closure glaucoma. Glaucoma type classification is important in glaucoma diagnosis.

Model-based optic nerve head segmentation on retinal fundus images.

The optic nerve head (optic disc) plays an important role in the diagnosis of retinal diseases. Automatic localization and segmentation of the optic disc is critical towards a good computer-aided diagnosis (CAD) system. In this paper, we propose a method that combines edge detection, the Circular Hough Transform and a statistical deformable model to detect the optic disc from retinal fundus images.

ORIGA(-light): an online retinal fundus image database for glaucoma analysis and research.

Retinal fundus image is an important modality to document the health of the retina and is widely used to diagnose ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. However, the enormous amount of retinal data obtained nowadays mostly stored locally; and the valuable embedded clinical knowledge is not efficiently exploited. In this paper we present an online depository, ORIGA(-light), which aims to share clinical groundtruth retinal images with the public; provide open access for researchers to benchmark their computer-aided segmentation algorithms.

Corneal graft detection for Descemet's stripping automated endothelial keratoplasty using optical coherence tomography.

The cornea is the window of the eye and when it is severely damaged or diseased, vision is impaired. Descemet's Stripping Automated Endothelial Keratoplasty (DSAEK) is a surgical procedure to replace the malfunctioned Descemet's membrane with a healthy one in order to restore the patient's sight. After the operation, ophthalmologists need to monitor the grafted membrane to check for signs of detachment, rejection, etc.

An automatic diagnosis system of nuclear cataract using slit-lamp images.

An automatic diagnosis system of nuclear cataract is presented in this paper. Nuclear cataract is graded according to the severity of opacity using slit-lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model (ASM).

Automatic fundus image classification for computer-aided diagonsis.

With the advances of computer technology, more and more computer-aided diagnosis (CAD) systems have been developed to provide the "second opinion". This paper reports an automatic fundus image classification technique that is designed to screen out the severely degraded fundus images that cannot be processed by traditional CAD systems. The proposed technique classifies fundus images based on the image range property.

Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis.

Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup.