This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

http://rabida.uhu.es/dspace/bitstream/handle/10272/4447/new_supervised_method_for_blood_vessel.pdf?sequence=2

A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Computer-aided diagnosis of diabetic retinopathy: A review

Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2012.0455

Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.

/pdf/fast-retinal-vessel-detection-and-measurement-using-wavelets-1l37h7fr5x.pdf

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

https://journals.ju.edu.jo/DirasatSci/article/viewFile/1984/1962

Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images

Cloud computing aims to maximize the benefit of distributed resources and aggregate them to achieve higher throughput to solve large scale computation problems. In this technology, the customers rent the resources and only pay per use. Job scheduling is one of the biggest issues in cloud computing. Scheduling of users’ requests means how to allocate resources to these requests to finish the tasks in minimum time. The main task of job scheduling system is to find the best resources for user’s jobs, taking into consideration some statistics and dynamic parameters restrictions of users’ jobs. In this research, we introduce cloud computing, genetic algorithm and artificial neural networks, and then review the literature of cloud job scheduling. Many researchers in the literature tried to solve the cloud job scheduling using different techniques. Most of them use artificial intelligence techniques such as genetic algorithm and ant colony to solve the problem of job scheduling and to find the optimal distribution of resources. Unfortunately, there are still some problems in this research area. Therefore, we propose implementing artificial neural networks to optimize the job scheduling results in cloud as it can find new set of classifications not only search within the available set.

/pdf/job-scheduling-for-cloud-computing-using-neural-networks-26ayc2ehio.pdf

Job Scheduling for Cloud Computing Using Neural Networks

Social Networks Sites (SNSs) are dominating all internet users’ generations, especially the students’ communities. Consequently, academic institutions are increasingly using SNSs which leads to emerge a crucial question regarding the impact of SNSs on students’ academic performance. This research investigates how and to what degree the use of SNSs affects the students’ academic performance. The current research’s data was conducted by using drop and collect surveys on a large population from the University of Jordan. 366 undergraduate students answered the survey from different faculties at the university. In order to study the impact of SNSs on student’s academic performance, the research hypotheses was tested by using descriptive analysis, T-test and ANOVA. Research results showed that there was a significant impact of SNS on the student’s academic performance. Also, there was a significant impact of SNS use per week on the student’s academic performance, whereas no differences found in the impact of use of SNSs on academic performance due to age, academic achievement, and use per day to most used sites. The findings of this research can be used to suggest future strategies in enhancing student’s awareness in efficient time management and better multitasking that can lead to improving study activities and academic achievements.

/pdf/the-impact-of-social-media-networks-websites-usage-on-2ewkmz49pr.pdf

The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

Since Knowledge Management (KM) is increasingly becoming an integral business function for lots of firms, as the latter recognize that competitiveness depends upon the effective management of intellectual resources, this paper aims to propose a theoretical model identifying the extent to which performing leadership development and its enablers in terms of developing training programs, possessing social capital skills, possessing human capital skills, settings goals, and deploying unique experiences; that are believed to enable knowledge management capability put into effect in the Jordanian public shareholding firms, and its impact on firm performance.

/pdf/a-theoretical-perspective-on-the-relationship-between-4qlmb2yc1a.pdf

A Theoretical Perspective on the Relationship between Leadership Development, Knowledge Management Capability, and Firm Performance

The vulnerability of digital audio signals for different types of risks requires an imperceptible and robust digital audio watermarking scheme. In this research, we propose creating one such imperceptible and robust hybrid watermarking scheme based on a discrete wavelet transform (DWT) and Schur decomposition hybrid method. The proposed scheme embeds the foreground bits of the watermarking image into the least significant bit of the diagonal coefficients of the triangular matrix S generated from Schur decomposition. Schur decomposition is applied on the second sub-band HL2 generated from applying a second-level 2D-Haar DWT on the first channel of the original audio signal. We analyze the proposed digital audio watermarking scheme’s performance in terms of signal to noise ratio (SNR), objective difference grades (ODG), and subjective difference grades (SDG) that resulting 81.43, 4.78 and 0.184, respectively. The resulting of payload capacity, NC, and BER are as high as 319.29 bps, 0.9911, and 0.0135, respectively. Experimental results confirm that the proposed scheme is inaudible and robust against common types of attacks such as Gaussian noise, re-quantization, re-sampling, low-pass filter, high-pass filter, echo, MP3 compression, and cropping. In comparison with state-of-the-art audio watermarking schemes, the proposed scheme’s performance is superior in term of imperceptibility, robustness, and data payload size.

Huda Karajeh

Papers

Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images

Job Scheduling for Cloud Computing Using Neural Networks

The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

A Theoretical Perspective on the Relationship between Leadership Development, Knowledge Management Capability, and Firm Performance

A robust digital audio watermarking scheme based on DWT and Schur decomposition