Mazani Manaf

Seed-based region growing study for brain abnormalities segmentation

This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used controlled experimental data as our testing data. The data is designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation was done with twenty data of each category. The knowledge of the size of the abnormalities by the number of pixels were then used as the ground truth to compare with the SBRG segmentation results. The proposed SBRG technique was found to produce potential solutions to the current difficulties in detecting abnormalities in the human brain tissue area.

Empirical study of brain segmentation using particle swarm optimization

This study uses an empirical study of the efficiency of Particle swarm optimization (PSO) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this study, we used controlled experimental data as our testing data. The data is designed which that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation is done with twenty data of each category. The knowledge of the size of the abnormalities by number of pixels are then used as the ground truth to compare with the PSO segmentation results. The proposed PSO technique is found to produce potential solutions to the current difficulties in detecting abnormalities in human brain tissue area as it produced promising segmentation outcomes for light abnormalities. Nevertheless, the PSO produced poor performance in dark abnormalities segmentation as it produces low correlation values in all conditions.

Computer Security Threats Towards the E-Learning System Assets

E-learning system is a web-based system which is exposed to computer threats. Services or asset of the e-learning system must be protected from any computer threats to ensure the users have peace of mind when using it. It is important to identify and understand the threats to the system in order develop a secure system. The main objectives of this paper are to discuss the computer security threats towards the e-learning system assets and to study the six categories of computer security threats to the e-learning assets. The activities which involve the e-learning assets will be analyzed and evaluated using the STRIDE model. The results show that the e-learning system assets are exposed to threats on availability, integrity and confidentiality .Our findings also show that the high risk assets are assessment and students’ assessment marks.

CR images of metacarpel cortical edge detection-bone profile histogram approximation method

The conventional criterion for fracture risk assessment is measured based on bone mineral density (BMD). These are measured using bone densitometry machines. Although there is a strong association between bone strength and BMD, it cannot sufficiently predict fracture risk in osteoperotic patients. In view of this a more accurate measurement of bone strength is required. Bone strength are measured by geometric measurement of the bone cortical area. Cortical thickness can be measured by finding the edges of the endosteal and the periosteal of the cortical bone. Image segmentation methods could be used to find these edges. This paper presents a method of finding these edges from the radiograph of non-dominant hand. Measurements are made from metacarpal two, three and four. The edge detection module developed is based on bone profile histogram approximation algorithm. However better cortical outline can only be obtained after preprocessing the images with smoothing filters. Evaluation is done by comparing the measurements of the inner and outer cortical diameter obtained from the processed images with manual measurements using mirocalipers.

Achieving trust in cloud computing using secure data provenance

Cloud computing is the next generation computer system which extends network architecture into dynamic and large scale capacity by using visualization techniques. Transparent and secure data provenance will enhance the level of trust in the Cloud. Recent literature on securing data provenance focuses only on providing partial part of security elements in their mechanisms, thus they could not provide full protection to the data provenance as a whole. This paper presents the provenance description and challenges in providing security assurances in the Cloud. The paper also proposes a novel trust model for data provenance in cloud computing. The model will be used for securing the transaction process of storing and accessing the data provenance. The paper describes in detail the flow process of the model to achieve a high level of trust in cloud services. The significance of this research is to enhance the level of trust in the cloud services via comprehensive trust model which consists all aspects of security elements in the model.

Fusion of fuzzy heuristic and particle swarm optimization as an edge detector

The purpose of this paper is to study the effectiveness of a fusion of fuzzy heuristic edge detection technique into particle swarm optimization. In this paper we experiment on various fuzzy membership threshold values to understand the impact of these threshold on the final optimized edge detected. The testing data used are hand radiograph images. The main aim of the experiment is to detect the left and right outer cortical (OC) and the left and right inner cortical (IC). The success of the edge detection method is determined using a line scan across the longitudinal of the metacarpal. It is consider successful when all four compete edges are detected. Eighty line scan are taken from each metacarpal images. The evaluation of the complete line scan demonstrates a high success rate in detecting the bone edges.

Adapting MapReduce framework for genetic algorithm with large population

Genetic algorithm (GA) is an algorithm that models inspiration from natural evolution to solve complex problems. GA is renowned for its ability to optimize different types of problem. However, the performance of GA necessitates data and process intensive computing when incorporating large population. This research proposes and evaluates the performance of GA by adapting MapReduce (MR), a parallel processing framework introduced by Google that utilize commodity hardware. The algorithm is executed with population size of up to 10 million. Performance scalability is tested by using 1, 2, 3, and 4 node configurations. The travelling salesman problem (TSP) is chosen as the case study while performance improvement, speedup, and efficiency are employed for performance benchmarking. This research revealed that MR can be naturally adapted for GA. It is also discovered that MR can accommodate GA with large population while providing good performance and scalability.

Gaussian Rule Based Fuzzy (GRBF) Membership Edge Detection on Hand Phantom Radiograph Images

This paper introduces the Gaussian shaped membership function to refine the Rule Based Fuzzy (RBF) image detection. It is expected that the proposed algorithm Gaussian Rule Based Fuzzy (GRBF) method can further refined the detection of periosteal and endosteal edges of hand phantom radiographs. The experimental data consists of four sets of hand phantom radiograph. Only metacarpal 2, 3 and 4 are considered. All of these data are processed with both GRBF and RBF. Mean and median filters are used as preprocessing tools. The results are compared both subjectively and statistically. The periosteal (strong edges) are well detected by both RBF and GRBF. However GRBF performs better than RBF in detecting the endosteal edges (weak edges).

Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives

Experimenting and testing vehicular network requires intensive labor and high expenses. Hence, an alternative solution is to use the simulation before actual implementation. Many studies have shown that mobility models, driver behavior and modeling of wireless channel have considerable effect on the performance results. Therefore, realistic mobility models and network simulators for vehicular ad-hoc networks (VANETs) are critical tools for research in order to accurately achieve desired results that reflect the realistic behavior of vehicular traffic. Due to the aforementioned reasons, we motivated to conduct a survey on VANET simulators. Thus, this paper explores main trends in simulation tools, discusses the weaknesses of current VANET simulators and provides recommendations to researchers to select which VANET simulators to be used.

Multi-lobed double-clad Erbium-Ytterbium co-doped Q-switched fiber laser based on nonlinear polarisation rotation technique

In this paper, we experimentally demonstrate a stable passive Q-switched fiber laser operating at 1543.5 nm using a double clad Erbium-Ytterbium co-doped fiber (EYDF) as the gain medium in conjunction with nonlinear polarization rotation (NPR) technique. An isolator is used in conjunction with a highly nonlinear EYDF to induce intensity dependent loss in a sufficiently-high loss ring cavity to achieve Q-switched operation with a low pump threshold of 300 mW. At 980 nm multimode pump power of 500 mW, the EYDF laser generates an optical pulse train with a repetition rate of 46.95 kHz, pulse width of 5.3 μs and pulse energy of 75.6 nJ. The simple and inexpensive Q-switched NPR-based laser has a big potential for applications in metrology, environmental sensing and biomedical diagnostics.