Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Computer aided analysis of dental radiographic images

This paper is a result of a fruitful cooperation between the computer science and the dental diagnosis experiences. The study presents a new approach of applying computer algorithms to radiographic images of dental implantation used for bone regeneration. We focus here only on the contribution of the computer assistance to the clinical research as the periodontal therapy is beyond the scope of this paper. The proposed system is based on a pattern recognition approach, directed to recognize density changes in the intra-bony affected areas of patients. It comprises different modules with new algorithms specially designed to treat the patients&rsquo; radiographic images more accurately. The system includes digitizing, detecting the complicated region of interest (ROI), defining reference area to correct any projection discrepancy of the follow up images, and finally to extract the distinguishing features of the ROI as a basis for determining the rate of new bone density accumulation. This study is applied to two typical dental cases for a patient who received two different operations. The results are very encouraging and more accurate than traditional techniques reported before. <br /

An Enhanced Adaptive Learning System based on Microservice Architecture

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian Petroleum Sector. After developing and strengthening the ALS using the cloud computing with the benefits of using Function as a Services “FaaS”, the functions are start rapidly in order to allow handling of individual requests by using the Microservice Architecture. This study includes a description of the statistic field study approach (The study’s community and its sample. As well as used tools, methodologies, and their validity and reliability. It also includes used procedures for tools codification and their application. Finally, statistical processes that were relied upon in study analysis)

CRYPTOSYSTEM FROM MULTIPLE BIOMETRIC MODALITIES

One of the most important parts of cryptographic systems is key generation. Researchers, for a long time period, have been inventing ways to produce tough and repeatable cryptographic keys. Keys that had these features are hard to be memorized and may be stolen or lost. For this purpose using biometric features to generate cryptographic key is the best way. Most previous Researchers focused to extract features and generate key from an individual biometric, but it is hard to be used in multi stages cryptographic systems. Therefore, this approach is enhancing the cryptographic systems by using long and complex cryptographic keys that are hard to be guessed and do not need to be memorized and provide better usage in multi stages cryptographic systems by extracting features from multi biometrics, That provides accuracy 99.83% with time less than using individual biometric by 90%

Automatic Segmentation of Optic Disc in Eye Fundus Images : a Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Visible/Infrared face spoofing detection using texture descriptors

With extensive applications of face recognition technologies, face anti-spoofing played an important role and has drawn a great attention in the security systems. This study represents a multi-spectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging. Spectral imaging is the capture of images in multiple bands. Since these attacks are carried out at the sensor, operating in the visible range, a sensor operating in another band can give more cues regarding the artifact or disguise used to carry out the attack. Our experimental results of public datasets proved that the proposed algorithms gain promising results for different testing scenarios and that our methods can deal with different illuminations and both photo and screen spoofing