Pellet Size Distribution Using Circular Hough Transform in Simulink

IEEE J. Transl. Eng. Health Med.

El-Sayed

2019

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.

Section: Methods and Proceduresmentioning

confidence: 99%

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

IEEE J. Transl. Eng. Health Med.

El-Sayed

2019

“…After that, a morphological reconstruction technique [21] is applied on the red layer image in order to remove the light reflection (which is often seen as a bright circle) on the pupil. Removing the light reflection here is considered as an important step since we will use the circular Hough transform (CHT) technique [22] to detect the pupil and iris. Then, local adaptive thresholding [23] is applied to separate the foreground from the background.…”

Section: Preprocessingmentioning

confidence: 99%

A smart intraocular pressure risk assessment framework using frontal eye image analysis

El-Sayed

2018

J Image Video Proc.

Intraocular pressure (IOP) in general refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions/symptoms that may lead to certain diseases such as glaucoma and therefore must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. A new smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images. The framework monitors the status of IOP risk by analyzing frontal eye images using image processing and machine learning techniques. A database of images collected from Princess Basma Hospital in Jordan was used in this work. The database contains 400 eye images: 200 images with normal IOP and 200 high eye pressure case images. The framework extracts five features from the frontal eye image: the pupil and iris diameter ratio, mean redness level of the sclera, red area percentage of the sclera, and two other features measured from the extracted contour of the sclera (contour height and contour area). Once the features were extracted, a neural network is trained and tested to obtain the status of the patients in terms of eye pressure. The framework detects the status of IOP (normal or high IOP) and produces evidence of the relationship between the five extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques using frontal eye images.

“…9. Accumulator Space for CHT [13] The dark blue circles shown in Fig. 9 represent a group of edge-points in the image being processed.…”

Section: G Iris Segmentationmentioning

confidence: 99%

Determination for Glaucoma disease based on red area percentage

2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

2016

Glaucoma is a disease that destroys the optic nerve of the eye. As a result, it causes vision loss or blindness. However, with immediate diagnosis and treatment, eyes can be protected against severe vision loss. Most vision loss cases due to Glaucoma are preventable if the disease is treated in the early stages. Most Glaucoma cases happen without signs and symptoms because peripheral vision can be damaged before an individual's central vision is affected. The existing procedures to detect Glaucoma are time consuming and inconclusive at the clinic. Thus, the detection can benefit from an automated computer vision-based technique of eye images. Nevertheless, the major problem of intraocular pressure (IOP) that leads to Glaucoma goes under detected. This paper proposes an automatic risk detection process of Glaucoma by extracting a portion of the eye sclera and measuring the red area percentage. The database used in this research was acquired from Princess Basma Hospital containing one hundred facial images divided into fifty healthy cases and fifty non-healthy cases that have high pressure in the eye.