Quantitative Comparison of Color Asymmetry Features for Automatic Melanoma Detection.

Asymmetry assessment is an important step towards melanoma detection. This paper compares some of the color asymmetry features proposed in the literature which have been used to automatically detect melanoma from color images. A total of nine features were evaluated based on their accuracy in predicting lesion asymmetry on a dataset of 277 images.

Role of the Choroid in Automated Age-related Macular Degeneration Detection from Optical Coherence Tomography Images.

Automatic detection of age-related macular degeneration (AMD) from optical coherence tomography (OCT) images is often performed using the retinal layers only and choroid is excluded from the analysis. This is because symptoms of AMD manifest in the choroid only in the later stages and clinical literature is divided over the role of the choroid in detecting earlier stages of AMD. However, more recent clinical research suggests that choroid is affected at a much earlier stage.

Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications.

This paper proposes a deep learning image segmentation method for the purpose of segmenting wound-bed regions from the background. Our contributions include proposing a fast and efficient convolutional neural networks (CNN)-based segmentation network that has much smaller number of parameters than U-Net (only 18.1% that of U-Net, and hence the trained model has much smaller file size as well).

An automatic quantitative measurement method for performance assessment of retina image registration algorithms.

This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature.

An augmented reality assistance platform for eye laser surgery.

This paper presents a novel augmented reality assistance platform for eye laser surgery. The aims of the proposed system are for the application of assisting eye doctors in pre-planning as well as providing guidance and protection during laser surgery. We developed algorithms to automatically register multi-modal images, detect macula and optic disc regions, and demarcate these as protected areas from laser surgery.

A cloud-based system for automatic glaucoma screening.

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases including glaucoma. However, these systems are usually standalone software with basic functions only, limiting their usage in a large scale. In this paper, we introduce an online cloud-based system for automatic glaucoma screening through the use of medical image-based pattern classification technologies.

Automatic retinal interest evaluation system (ARIES).

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. However, in practice, retinal image quality is a big concern as automatic systems without consideration of degraded image quality will likely generate unreliable results. In this paper, an automatic retinal image quality assessment system (ARIES) is introduced to assess both image quality of the whole image and focal regions of interest.

Geometric corner extraction in retinal fundus images.

This paper presents a novel approach of finding corner features between retinal fundus images. Such images are relatively textureless and comprising uneven shades which render state-of-the-art approaches e.g.

Automated anterior chamber angle localization and glaucoma type classification in OCT images.

To identify glaucoma type with OCT (optical coherence tomography) images, we present an image processing and machine learning based framework to localize and classify anterior chamber angle (ACA) accurately and efficiently. In digital OCT photographs, our method automatically localizes the ACA region, which is the primary structural image cue for clinically identifying glaucoma type. Next, visual features are extracted from this region to classify the angle as open angle (OA) or angle-closure (AC).

Integrating research, clinical practice and translation: the Singapore experience.

We introduce the experiences of the Singapore ocular imaging team, iMED, in integrating image processing and computer-aided diagnosis research with clinical practice and knowledge, towards the development of ocular image processing technologies for clinical usage with potential impact. In this paper, we outline key areas of research with their corresponding image modalities, as well as providing a systematic introduction of the datasets used for validation.

Self-assessment for optic disc segmentation.

Optic disc segmentation from retinal fundus image is a fundamental but important step in many applications such as automated glaucoma diagnosis. Very often, one method might work well on many images but fail on some other images and it is difficult to have a single method or model to cover all scenarios. Therefore, it is important to combine results from several methods to minimize the risk of failure.

Validity of a new optic disc grading software for use in clinical and epidemiological research.

Background: To determine the reliability and agreement of a new optic disc grading software program for use in clinical, epidemiological research.

Design: Reliability and agreement study.

Samples: 328 monoscopic and 85 stereoscopic optic disc images.

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.

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).

Superpixel classification for initialization in model based optic disc segmentation.

Optic disc segmentation in retinal fundus image is important in ocular image analysis and computer aided diagnosis. Because of the presence of peripapillary atrophy which affects the deformation, it is important to have a good initialization in deformable model based optic disc segmentation. In this paper, a superpixel classification based method is proposed for the initialization.

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.

Focal biologically inspired feature for glaucoma type classification.

Glaucoma is an optic nerve disease resulting in 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.

Closed angle glaucoma detection in RetCam images.

Closed/Open angle glaucoma classification is important for glaucoma diagnosis. RetCam is a new imaging modality that captures the image of iridocorneal angle for the classification. However, manual grading and analysis of the RetCam image is subjective and time consuming.

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.