Computer Vision Based Early Intraocular Pressure Assessment From Frontal Eye Images

Intraocular Pressure (IOP) in general, refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions or 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. Exiting IOP monitoring tools include eye tests at clinical facilities and computer-aided techniques from fundus and optic nerves images. In this work, a new computer vision-based smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images early-on. The framework determines the status of IOP by analyzing frontal eye images using image processing and machine learning techniques. A database of images from the Princess Basma Hospital was used in this work. The database contains 400 eye images; 200 images with normal IOP and 200 high eye pressure case images. This study proposes novel features for IOP determination from two experiments. The first experiment extracts the sclera using circular hough transform, after which four features are extracted from the whole sclera. These features are mean redness level, red area percentage, contour area and contour height. The pupil/iris diameter ratio feature is also extracted from the frontal eye image after a series of pre-processing techniques. The second experiment extracts the sclera and iris segment using a fully conventional neural network technique, after which six features are extracted from only part of the segmented sclera and iris. The features include mean redness level, red area percentage, contour area, contour distance and contour angle along with the pupil/iris diameter ratio. Once the features are extracted, classification techniques are applied in order to train and test the images and features to obtain the status of the patients in terms of eye pressure. For the first experiment, neural network and support vector machine algorithms were adopted in order to detect the status of intraocular pressure. The second experiment adopted support vector machine and decision tree algorithms to detect the status of intraocular pressure. For both experiments, the framework detects the status of IOP (normal or high IOP) with high accuracies. This computer vison-based approach produces evidence of the relationship between the extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques from frontal eye images

Image-Based Risk Assessment Analysis for Glaucoma Determination

Glaucoma is the most common cause of blindness in the world, and it is known as the silent thief of vision because it can sneak up on any patient. However, the loss of vision from Glaucoma is preventable. Glaucoma is caused by the gradual increase of pressure in the eye which is known as Intraocular Pressure (IOP). While the pressure increases in the eye, different parts of the eye become affected until the eye parts are damaged. The eye vessels' sizes are so small that they easily become affected. Moreover, the pressure inside the eye pushes the lens affecting the size of the Pupil. Also, the pressure in the eye presses the optic nerve in the back of the eye causing damage to the nerve fibers. Over 90% of Glaucoma cases have no signs or symptoms because peripheral vision can be lost before a person's central vision is affected. The only way to prevent Glaucoma is by early detection. This research study calculates three features from the frontal eye image that can be used to assess the risk of Glaucoma. These features include redness of the sclera, red area percentage, and the Pupil size. The database used in the work contains 100 facial images that have been divided into 50 healthy cases and 50 non-healthy cases with high eye pressure. Once the features were extracted, a neural network classification is applied to obtain the status of the patients in terms of eye pressure

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature

A Smart Intraocular Pressure Risk Assessment Framework Using Frontal Eye Image Analysis

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.https://doi.org/10.1186/s13640-018-0334-

The Effectiveness of Using the Internet of Things in Developing Scientific Concepts for the Sixth Basic Grade Students in Mathematics and their Attitudes Towards the Internet of Things فاعلية استخدام إنترنت الأشياء في تنمية المفاهيم العلمية لدى طلبة الصف السادس الأساسي في مادة الرياضيات واتجاهاتهم نحو إنترنت الأشياء

Abstract: This study aimed to show the effect of using the internet of things (IoT) in developing scientific concepts for sixth grade in basic mathematics and their attitudes towards the internet of things. the study sample consisted of (60) male and female students from the sixth basic class in Mayar international schools of the Directorate of Special Education in the Capital Governorate in Jordan, during the second semester of the academic year 2019/2020. Two classes were chosen and randomly divided into two groups, one experimental consisting of (30) male and female students who studied using the internet of things, and the other consisted of (30) male and female students studied in the traditional method. To achieve the objective of the study, the internet of things application was prepared, and the development of scientific concepts was tested. The results of the study indicated the presence of statistically significant differences between the mean of students ’scores at the level of significance (α = 0.05) in the experimental and control groups in the post-test and in favor of the experimental group, while the results showed that there were no statistically significant differences due to the difference in gender, and also showed that there was a difference statistically significant between the pre- and post-tests at the significance level (α = 0.05). The differences came in favor of the post-test for the experimental group students. The study recommended that attention be paid to providing internet applications of things in teaching various subjects, in the basic stage. ملخص: هدفت هذه الدراسة إلى الكشف عن فاعلية استخدام إنترنت الأشياء في تنمية المفاهيم العلمية لدى طلبة الصف السادس الأساسي في مادة الرياضيات واتجاهاتهم نحو إنترنت الأشياء. وقد تكون أفراد الدراسة من (60) طالباً وطالبة من طلبة الصف السادس الأساسي في مدارس ميار الدولية التابعة لمديرية التعليم الخاص في محافظة العاصمة في الأردن، خلال الفصل الثاني من العام الدراسي 2019/2020. تم اختيار شعبتين وتقسيمهم عشوائياً إلى مجموعتين إحداهما تجريبية تكونت من (30) طالباً وطالبة درست باستخدام إنترنت الأشياء، والأخرى ضابطة تكونت من (30) طالباً وطالبة درست بالطريقة الاعتيادية. ولتحقيق هدف الدراسة تم إعداد تطبيق إنترنت الأشياء، واختبار تنمية المفاهيم العلمية. أشارت نتائج الدراسة إلى وجود فروق ذات دلالة إحصائية بين متوسطي درجات الطلبة عند مستوى الدلالة (α=0.05) في المجموعتين التجريبية والضابطة في الاختبار البعدي ولصالح المجموعة التجريبية، في حين بينت النتائج عدم وجود فروق دالة إحصائياً تعزى لاختلاف الجنس، كما أظهرت وجود فرق دال إحصائياً بين الاختبارين القبلي والبعدي عند مستوى الدلالة (α=0.05) لصالح الاختبار البعدي لطلبة المجموعة التجريبية. أوصت الدراسة بالاهتمام بتوفير تطبيقات إنترنت الأشياء في تدريس المواد الدراسية المختلفة، وفي مراحل دراسية أخرى

Design REA Ontology For Knowledge Sharing In IT Project

The Resources-Events-Agents (REA) model is a semantic data model for the development and integration of conceptual schemas of accounting information systems. This paper is to change the look of REA modeling and to test the REA as a conceptual design, this study is to model the knowledge sharing mechanism in KPT system of SerindIT Company using REA component, also to use the Protege OWL software as a tool to validate the REA ontology on the selected case which is Knowledge sharing mechanism adopted in KPT system

Kliničeskaja medicina : naučno-praktičeskij žurnal = Clinical medicine (Russian journal)

Most individuals do not realize they have a broken vein in the eye until somebody lets them know or they look in a mirror. This condition is not tormenting, but commonly creates obtuse trauma to the eye. Treatment is often not required for subconjunctival drainage. In the event that a patient has recognized the presence of blood in his or her eye, it may be fitting for him or her to look for medical consideration. While a subconjunctival drain is occasionally risky, hyphema (blood in the front assembly of the eye, between the cornea and the iris) is conceivably a more serious condition, with more serious outcomes. This work first provides an overview of the most common techniques used to calculate the blood vessels in color images of the retina. Then, it presents a study that has been conducted to discuss the early steps of the intraocular pressure (IOP) detection in the eye, using histogram analysis

Generation of the First Structure-Based Pharmacophore Model Containing a Selective “Zinc Binding Group” Feature to Identify Potential Glyoxalase-1 Inhibitors

Within this study, a unique 3D structure-based pharmacophore model of the enzyme glyoxalase-1 (Glo-1) has been revealed. Glo-1 is considered a zinc metalloenzyme in which the inhibitor binding with zinc atom at the active site is crucial. To our knowledge, this is the first pharmacophore model that has a selective feature for a “zinc binding group” which has been customized within the structure-based pharmacophore model of Glo-1 to extract ligands that possess functional groups able to bind zinc atom solely from database screening. In addition, an extensive 2D similarity search using three diverse similarity techniques (Tanimoto, Dice, Cosine) has been performed over the commercially available “Zinc Clean Drug-Like Database” that contains around 10 million compounds to help find suitable inhibitors for this enzyme based on known inhibitors from the literature. The resultant hits were mapped over the structure based pharmacophore and the successful hits were further docked using three docking programs with different pose fitting and scoring techniques (GOLD, LibDock, CDOCKER). Nine candidates were suggested to be novel Glo-1 inhibitors containing the “zinc binding group” with the highest consensus scoring from docking