PURPOSE: This study investigates the impact of field of view (FOV) on the diagnosis of Plus disease in retinopathy of prematurity (ROP), focusing on how different FOVs influence expert assessments. METHODS: Fundus images from 91 ROP infants were captured using a RetCam, each including the optic disk (OD) and a ridge/demarcation line. Cropped versions with a radius of three disc diameters (3DD) centered on the OD were automatically extracted using a computer-based algorithm. Images were categorized into five (normal, pre-Plus, Plus1, Plus2, Plus3), three (non-Plus, pre-Plus, Plus), or two groups (non-Plus, Plus). Five experts graded both entire-view and 3DD images. Inter-expert reliability was assessed using Cronbach’s alpha. RESULTS: Seventy-four 3DD and 77 entire-view images were graded. Cronbach’s alpha values were 0.933 (entire-view) and 0.942 (3DD) in the 5-level grading system, indicating slightly higher consistency among experts for 3DD images. Notable diagnostic shifts were observed when comparing entire-view and 3DD images, with 25.0% of Plus1 diagnoses shifting to pre-Plus and 16.9% of pre-Plus diagnoses shifting to normal in the 5-level grading system. Two experts showed substantial changes in their grades, with one expert upscaling 33.7% and downscaling 16.9% of diagnoses, and the other upscaling 21.7% and downscaling 15.7% when assessing entire-view compared to 3DD images. CONCLUSIONS: FOV significantly affects the diagnosis of Plus disease, with peripheral retinal features influencing expert judgments. Restricting the FOV to a 3DD area improved inter-expert reliability but led to notable diagnostic shifts. These findings highlight the need for standardized imaging protocols and the potential role of AI in reducing diagnostic variability in ROP.

Plus disease is characterized by abnormal changes in retinal vasculature of premature infants. Presence of Plus disease is an important criterion for identifying treatment-requiring cases in Retinopathy of Prematurity (ROP). However, diagnosis of Plus disease has been shown to be subjective and there is wide variability in the classification of Plus disease by ROP experts, which is mainly because experts have different cut-points for distinguishing the levels of vascular abnormality. This suggests that a continuous Plus disease severity score may reflect more accurately the behavior of expert clinicians and may better standardize the classification. The effect of using quantitative methods and computer-based image analysis to improve the objectivity of Plus disease diagnosis have been well established. Nevertheless, the current methods are based on categorical classifications of the disease severity and lack the compatibility with the continuous nature of the abnormal changes in retinal vasculatures. In this study, we developed a computer-based method that performs a quantitative analysis of vascular characteristics associated with Plus disease and utilizes them to build a regression model that outputs a continuous spectrum of Plus severity. We evaluated the proposed method against the consensus diagnosis made by four ROP experts on 76 posterior ROP images. The findings of our study indicate that our approach demonstrated a relatively acceptable level of accuracy in evaluating the severity of Plus disease, which is comparable to the diagnostic abilities of experts.

The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.

This study, investigates the relationship between retinal image features and β-amyloid (Aβ) burden in the brain with the aim of developing a non-invasive early detection method for Alzheimer’s disease (AD). 172 retinal images from 20 clinically probable AD and 45 age-matched control cases were acquired using a hyper spectral imaging system. Brain Aβ accumulation was estimated from amyloid PET imaging. Spatial and spectral features from the hyperspectral retinal images were calculated including vessels tortuosity and image textures at different anatomical regions. Retinal veins of amyloid positive subjects (Aβ+) showed a higher mean tortuosity compared to the amyloid negative subjects (p<2.4e-7). Furthermore, a significant difference between texture measures of retinal arteries and their adjacent regions were observed in Aβ+ subjects when compared to the Aβ- (p<1.3e-5).

AbstractIntroductionThis study investigates the relationship between retinal image features and β‐amyloid (Aβ) burden in the brain with the aim of developing a noninvasive method to predict the deposition of Aβ in the brain of patients with Alzheimer's disease.MethodsRetinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired (3 images per subject) using a hyperspectral retinal camera. The cerebral amyloid status was determined from binary reads by a panel of 3 expert raters on 18F‐florbetaben positron‐emission tomography (PET) studies. Image features from the hyperspectral retinal images were calculated, including vessels tortuosity and diameter and spatial‐spectral texture measures in different retinal anatomical regions.ResultsRetinal venules of amyloid‐positive subjects (Aβ+) showed a higher mean tortuosity compared with the amyloid‐negative (Aβ−) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ− subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between texture measures built over retinal arterioles and their adjacent regions were observed in Aβ+ subjects when compared with the Aβ−. A classifier was trained to automatically discriminate subjects combining the extracted features. The classifier could discern Aβ+ subjects from Aβ− subjects with an accuracy of 85%.DiscussionSignificant differences in texture measures were observed in the spectral range 450 to 550 nm which is known as the spectral region known to be affected by scattering from amyloid aggregates in the retina. This study suggests that the inclusion of metrics related to the retinal vasculature and tissue‐related textures extracted from vessels and surrounding regions could improve the discrimination performance of the cerebral amyloid status.

Le fait que l'oeil puisse etre visualise de maniere non invasive ouvre des possibilites de mesure de biomarqueurs pour le diagnostic de conditions a long terme. Selon de nombreuses etudes, plusieurs maladies cardiovasculaires et neurodegeneratives telles que la maladie d’Alzheimer (AD) et l’atherosclerose (ATH) se manifestent dans la retine sous forme de modifications morphologiques pathologiques et / ou vasculaires. Des methodes d'imagerie oculaire en deux dimensions et des techniques de tomographie par coherence optique (OCT) en trois dimensions ont ete developpees pour fournir des descriptions des structures retiniennes. Cependant, les images acquises par ces techniques permettent principalement de mesurer les caracteristiques spatiales et pas la variance relative de l’intensite des pixels sur differentes longueurs d’onde, de sorte que d’importantes caracteristiques liees aux tissus peuvent encore rester a decouvrir. Dans cette etude, une camera retinienne metabolique hyperspectrale (MHRC) a ete utilisee pour permettre l'acquisition d'une serie d'images retiniennes obtenues a des longueurs d'onde specifiques couvrant le spectre du visible au proche infrarouge (NIR). Dans cette technique, le facteur de transmission, l'absorption et la diffusion de la lumiere sont refletes dans le spectre de la lumiere emise par le tissu. Par consequent, non seulement les caracteristiques spatiales communes mais egalement les « signatures spectrales » de biomolecules pourraient etre revelees. Cela aide a trouver une plus grande variete de caracteristiques spatiales / spectrales pour une investigation plus precise des biomarqueurs retiniens des maladies. En ce qui concerne les couts et les limites associes aux diagnostics actuels de l’AD et de l’ATH, le but de cette these etait d’analyser le contenu en informations d’images retiniennes hyperspectrales riches en donnees dans le but de caracteriser des informations discriminantes cachees liees aux tissus afin d’identifier des biomarqueurs possibles de ces deux maladies. A cette fin, une combinaison de caracteristiques vasculaires et de mesures de textures spatiales-spectrales ont ete extraites de differentes regions anatomiques de la retine. Dans le contexte de la maladie d'Alzheimer, des images retiniennes de 20 cas presentant une alteration cognitive et de 26 cas normaux cognitivement ont ete acquises a l'aide de la camera MHRC. Le statut amyloide cerebral a ete determine a partir de lectures binaires effectuees par un panel de 3 experts noteurs ayant participe a des etudes de TEP au 18F-Florbetaben. Des caracteristiques de l’image retinienne ont ete calculees, notamment la tortuosite et le diametre des vaisseaux, ainsi que les mesures de textures spatiales-spectrales sur les arterioles, les veinules et le tissu environnant. Les veinules retiniennes des sujets amyloides positifs (Aβ +) ont presente une tortuosite moyenne plus elevee par rapport aux sujets amyloides negatifs (Aβ-). Le diametre arteriolaire des sujets Aβ + s'est avere superieur a celui des sujets Aβ- dans une zone adjacente a la tete du nerf optique. De plus, une difference significative entre les mesures de texture construites sur les arterioles retiniennes et leurs regions adjacentes a ete observee chez les sujets Aβ + par rapport aux Aβ-. Dans le contexte de l'ATH, 60 images retiniennes de 30 ATH probables sur le plan clinique et 30 cas de controle ont ete acquises. Les criteres d'inclusion pour les sujets souffrant d'ATH comprenaient: l'infarctus du myocarde; angiographie coronaire montrant au moins une stenose coronaire (plus de 50%); et / ou une angioplastie coronaire; et /ou pontage coronaire. Les arterioles retiniennes des sujets ATH ont montre un retrecissement significatif par rapport aux sujets temoins. En outre, une difference significative entre les mesures de textures d'images prises sur les arterioles et les veinules retiniennes et leurs regions adjacentes a ete trouvee entre les sujets ATH et les sujets temoins. Nos etudes transversales ont montre que l’analyse hyperspectrale des images retiniennes pouvait discerner avec une precision acceptable l’AD et l’ATH des sujets temoins correspondants.----------ABSTRACT The fact that eye can be visualized non-invasively, opens up possibilities to measure biomarkers for diagnosis of long-term conditions. A significant body of literature has demonstrated that many of the neurodegenerative and cardiovascular diseases such as Alzheimer’s disease (AD) and atherosclerosis (ATH) manifest themselves in retina as pathological and/or vasculature morphological changes. Methods for two-dimensional fundus imaging and techniques for three-dimensional optical coherence tomography (OCT) have been developed to provide descriptions of retinal structures. However, images acquired by these techniques mostly allow for measuring the spatial characteristics of the tissue and lack of the relative variances across differing wavelengths, thus important spectral features may remain uncovered. In this study, a Metabolic Hyperspectral Retinal Camera (MHRC) was used that permits the acquisition of a series of retinal images obtained at specific wavelengths covering the visible and near infrared (NIR) spectrum. In this technique, light transmittance, absorption, and scatter are reflected in the spectrum of light emitted from the tissue. Use of MHRC in this study was aimed to extract not only the common spatial features but also “spectral signatures” of biomolecules in retinal tissue. Regarding the costs and limitations of the current diagnostic methods for AD and ATH, the purpose of this thesis was to analyze the information content of data-rich hyperspectral retinal images to characterize tissue-related discriminatory information to identify possible biomarkers of Alzheimer’s disease and atherosclerosis. To this end, a combination of vascular features and spatial/spectral texture measures were extracted from different anatomical regions of the retina. In the context of AD, retinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired using MHRC. The cerebral amyloid status was determined from binary reads by a panel of three expert raters on 18F-Florbetaben PET studies. Our approach did not aim to visualize directly Aβ deposits in the retina but rather to determine a likely amyloid status based on sets of retinal image features highly correlated with the cerebral amyloid status. Retinal image features were calculated including vessels’ tortuosity and diameter. Spatial/spectral texture measures over arterioles, venules, and tissue around were also extracted. Retinal venules of amyloid positive subjects (Aβ+) showed a higher mean tortuosity compared to the amyloid negative (Aβ-) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ- subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between spatial/spectral texture measures built over retinal arterioles and surrounding tissues were observed in Aβ+ subjects when compared to the Aβ-. In the context of ATH, 60 retinal images from 30 clinically probable ATH and 30 control cases were acquired. Inclusion criteria for subjects suffering from ATH included: myocardial infarction; coronary angiography showing at least one coronary stenosis (more than 50%); and/or coronary angioplasty; and/or coronary bypass. Retinal arterioles of ATH subjects showed a significant narrowing when compared to control subjects. Moreover, a significant difference between image texture measures taken over retinal arterioles and retinal venules and their adjacent regions was observed between ATH subjects and control subjects. Our cross-sectional studies have shown that hyperspectral retinal image analysis could be used to discriminate AD and ATH from corresponding control subjects based on a non-invasive eye scan.

Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application .

Business Process Reengineering increases enterprise's chance to survive in competition amongorganizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology.Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment

Software architectures evaluation has an important role in the life cycle of software systems.The conceptual integrity is one of the quality attributes which could be closely related to software architectural design.It is the underlying theme or vision that unifies all levels of the system's design.In this paper, a method for measuring the conceptual integrity of software architecture is provided.Conceptual integrity measurement is done in several steps by extracting a graph structure which its nodes are architectural concepts and its edges are relationship between them.The constructed graph is then weighted according to the type of relationship among the architectural concepts.Finally, a metric for evaluating the conceptual integrity from the refined graph is provided.

Enterprise Architecture is an approach for understanding, engineering, and managing all enterprise elements and their relationships. In order to better explain the concepts defined in the quality attributes in enterprise architecture and their relationships, the quality scenarios are used. Because of the breadth and variety of enterprise architecture quality scenarios, the cost of implementation scenarios is high. Therefore, prioritization and selection of optimal scenarios, in terms of quality attributes satisfaction, before implementation, is very important. Due to the diversity of stakeholders, large number of scenarios and possible selections, prioritization scenarios involves searching a large state space and considering all of the possible selections which is not precise. Genetic Algorithm is the intelligent algorithm that solves the problems based on metaheuristic search. This paper presents an innovative method for prioritizing quality scenarios, based on the knowledge and experience of stakeholders using genetic algorithm. The validity of proposed algorithm is evaluated in two case studies and it’s precision is compared with similar methods. The results of evaluation show the correctness and performance of this algorithm to prioritize large number of quality scenarios with higher precision and lower computational complexity in comparing to other methods. Keyword: Genetic Algorithm, Quality Scenario, Enterprise Architecture Evaluation

Over the past few years, a large number of models, ontologies and tools have been proposed to capture, share and the management of architectural knowledge (AK) and particularly architectural design decisions (ADD) as an important part of AK of a software-intensive system. However, the growing tendency in Globalization of Software Development sets the stage for new challenges in the management of AK in a geographically distributed context in which it seems the existing AK models and tools are no longer sufficient for such setting. In this paper we develop an ontology-based approach to manage AK in order to partly mitigate the deficiencies of existing AK approaches in a distributed software devotement.

In recent years, cloud computing growth has taken all the attention of various communities like researches, internet users, and government organizations. It reduced information technology overhead for the end-user, and total cost of ownership, and brought on greater flexibility, and on demand services. This concept, as a new kind of advanced technology, accelerates the innovation for the computer industry. This concept is based on the Internet, whose task is to ensure that users can simply use the computing resources. Users do not require any special knowledge about the concept of Cloud computing while using it. But before starting to use cloud services, they should choose between many service providers that are available nowadays. On the other hand, in this technologically growing world with lots of service providers and cloud services, it would be a difficult task for a user to choose between service providers. So a sever need of a Service Providers Selection System (SPS System) is felt. In this paper, we have proposed a method to select Service Providers in the best possible way, according to the cloud services that a user wants, and the priority of his/her needs. This selection is done by means of a service provider selection agent.

Development process divided into two categories: Traditional models and agile models, traditional models have heavy documentation and delay in time project but agile models being lightweight processes because have light weight documentation and heavy intercommunications therefore reduce the delays of the traditional models. They emphasize customer satisfaction, fast response to changes, and release in less time. In the last decades, to achieve high quality of system, use the architecture as an important matter in the development process. Therefore using software architecture skills in agile methods could improve them toward producing a software system that has an appropriate structure because focuses on achieving quality attributes for a software system. This paper is a review on accomplished proceedings that combine software architecture and agile methods to improve Software developments.

This paper introduces a new criterion to get better performance in selecting the most important sentences of text for extractive text summarization. There are two kinds of criteria to find the most relevant sentences of text: statistical criteria and semantic relations between text sentences. The proposed technique is a statistical criterion. The idea behind our approach is to consider the position of sentence words relative to words in the sentence occurring in title and keywords. We evaluate this criterion in combination with other statistical criteria. The results show that using this criterion in selecting the most important sentences of text has good results

Text summarization is a process that reduces the size of the text document and extracts significant sentences from a text document. We present a novel technique for text summarization. The originality of technique lies on exploiting local and global properties of words and identifying significant words. The local property of word can be considered as the sum of normalized term frequency multiplied by its weight and normalized number of sentences containing that word multiplied by its weight. If local score of a word is less than local score threshold, we remove that word. Global property can be thought of as maximum semantic similarity between a word and title words. Also we introduce an iterative algorithm to identify significant words. This algorithm converges to the fixed number of significant words after some iterations and the number of iterations strongly depends on the text document. We used a two-layered backpropagation neural network with three neurons in the hidden layer to calculate weights. The results show that this technique has better performance than MS-word 2007, baseline and Gistsumm summarizers.

Documentation of software architecture is a good approach to understand the architecture of a software system and to match it with the changes needed during the software maintenance phase. In several systems like legacy or older systems these documents are not available or if available; they are not up-to-date and usable. So, reconstruction of architecture in order to maintain these systems is a necessary activity. When we talk about software architecture, usually we are looking for a modular view of architecture with low coupling and high cohesion. In this paper, we try to improve the algorithm of machine-learning which is presented before for architecture recovery, and we propose using it for architecture reconstruction in order to obtain optimum modularity in the architecture with low coupling and high cohesion. The proposed algorithm is evaluated in a case study, and its results are presented.

Robocup competition is an international event for research on fully autonomous robot control and related subject like: Artificial intelligence, Image processing, robot path planning, and obstacle avoidance. In this paper new practical software based methods for control, analysis, decision making and trajectory of an autonomous robot in Middle Size Soccer Robot league (MSL) are presented. In a robots soccer game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the software of a soccer robot system should have the features of flexibility, real-time control and adaptation. For this purpose, we utilize the sensor data fusion method in the control system parameters, self localization and world modelling. A vision-based self-localization and the conventional odometry systems are fused for robust self-localization. The methods have been tested in the many Robocup competition field middle size robots. This paper has tried to focus on description of areas such as omni directional mechanisms, omni-vision sensor for object detection, robot path planning, multimedia database management system for sophisticated offline analysis and other subjects related to mobile robot's software. The results are satisfactory which has already been successfully implemented in ADRO RoboCup team. This project is still in progress and some new interesting methods are described in the current report.

Although a good architecture is not sufficient to guarantee the success of a software product, undoubtedly it is essential to support the product quality. Evaluating software architecture provides early insight into product capabilities and limitations. The earlier in the life cycle the problems are detected, the cheaper it is to fix them. In this paper, at first, a review on well-known scenario-based methods to evaluate software architectures is presented, and their advantages and limitations are discussed. Then, a method named SHADD with different characteristics is introduced to detect architectural defects. Using a scenario-based approach, the proposed method finds out the critical aspects and potential problems threatening the system from the stakeholder’s point of view. Scenarios are then used as a basis for the process of architectural defects detection. SHADD and its elements are specified in a systematic form and an illustration of its application on the architecture of a real system is also presented. The results show that SHADD can be used to detect those architectural defects which may be uncovered during the application of conventional evaluation methods.

In the past decade, significant advances have been gained in e-learning systems; nonetheless, learning management systems are not free of difficulties. As the most important of these problems it can be pointed out that there is no guaranty whether the actual student is present in the virtual classroom or not and also there is no way to track the students' attendance continually during class time. The main purpose of this paper is to present a new e-learning model based on web applications with attendance control ability. The presented multimodal biometric based model is used for identification, authentication and tracking the users. In this model two behavioral biometric characteristics (mouse movements and keystroke dynamics) and a physical one (face features) are used. A new algorithm is presented to demonstrate use of these three biometrics technologies. As the experiments' results show, in verification and attendance control processes the proposed solution needs a lower level of students' collaboration. After the implementation of this model it can be added to the existing LMS and become compatible with it. Therefore, this model can be used to track the continuous attendance of the users in sensitive stages of e-learning process. 1. INTRODUCTION E-learning systems represent a new form of learning and are becoming more and more popular every day , therefore the need for security in this system is very sensible. Lack of adequate tools to properly track the users' behavior is one of the most important problems in learning management systems (LMS) [1,6]. Traditional solutions that recorded the interaction between the users and systems cannot guarantee that the user is who he or she claims to be and also they cannot find out whether the user is in front of the computer or not [4]. Information that is registered in the traditional users' log files just contains the students' entry and exit time. [1, 4]. A user could log in to the LMS then leave the system after a few minutes and return near the end of class only to log out. However, it is clear that this act is not reflected in the log files. There are many situations similar to the one described above where the traditional solutions can not specify the actual time duration the user spends in front of the computer.