Ahmed Wasif Reza

Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds

The detection of bright objects such as optic disc (OD) and exudates in color fundus images is an important step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. In this paper, a novel approach to automatically segment the OD and exudates is proposed. The proposed algorithm makes use of the green component of the image and preprocessing steps such as average filtering, contrast adjustment, and thresholding. The other processing techniques used are morphological opening, extended maxima operator, minima imposition, and watershed transformation. The proposed algorithm is evaluated using the test images of STARE and DRIVE databases with fixed and variable thresholds. The images drawn by human expert are taken as the reference images. The proposed method yields sensitivity values as high as 96.7%, which are better than the results reported in the literature.

Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation

Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.

A DUAL-BAND OMNI-DIRECTIONAL MICROSTRIP ANTENNA

Omni-directional antennas are useful for variety of wireless communication devices as well as capable of handling the additional different frequency bands since the radiation pattern allows good transmission and reception from a mobile unit. However, to implement the two frequencies on a single antenna with wide bandwidth can be significant because of the presence of mutual coupling and interference effects between the two radiating elements. In this paper, a novel method of combining dual-band frequencies onto a single layer board with wide bandwidth is described. A dual-band printed dipole antenna is designed in this study by combining a rectangular and two “L” shaped radiating elements and are embedded on a single layer structure with relatively small size. The obtained results show that the proposed dual-band omni-directional microstrip antenna achieves high antenna efficiency and provides better bandwidth while maintaining the structural compact size.

A Novel Integrated Mathematical Approach of Ray-Tracing and Genetic Algorithm for Optimizing Indoor Wireless Coverage

While designing wireless networks, it is crucial to obtain the maximum coverage by using the minimum number of transmitting antennas. This paper proposes a new algorithm for determining the minimum number of transmitting antennas as well as their appropriate locations to provide the optimized wireless coverage in the indoor environment. The proposed algorithm uses a ray-tracing method to predict the signal distribution among the sampling points in the indoor area due to one or more transmitters and the genetic algorithm (GA) incorporated with the Breath First Search (BFS) terminology to determine the minimum number of transmitters and their corresponding locations to achieve the optimum wireless coverage. The proposed method outperforms the existing method in terms of both space and time complexities. The results obtained from this study also show that the computation time using the proposed algorithm is much less than that of the existing algorithm.

A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy

The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.

Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.

Extraction of the Contours of Optic Disc and Exudates Based on Marker-Controlled Watershed Segmentation

The optic disc (OD) and exudates form the main features of fundus images for diagnosing eye disease such as diabetic retinopathy and glaucoma. In this paper, an algorithm for the extraction of OD and exudates from fundus images based on marker controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. The results are compared with those reported earlier in the literature. It is shown that the proposed method can yield an average sensitivity value of 94%, which is higher than the value reported earlier.

A novel scheduling algorithm based on game theory and multicriteria decision making in LTE network

Fourth generation wireless networks provide mobile users with high data rate and quality of services, such as Long Term Evolution (LTE), which has been developed by the 3rd Generation Partnership Project (3GPP). However, 3GPP is not a standardized scheduling algorithm to utilize LTE properties in smart grid applications. This paper proposes a two-level scheduling scheme composed of cooperative game theory (bankruptcy and shapely) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). The proposed algorithm improves resource allocation for three smart grid applications, namely, voice, video surveillance, and metering data. On the first level, bankruptcy and shapely value algorithm fairly distribute the resources among smart grid applications. On the second level, TOPSIS algorithm allocates the resources among applications users based on their criteria and the applications preferences. Moreover, the systems performance has been evaluated in terms of throughput, delay, and fairness index. The proposed algorithm is compared with existing algorithms, such as proportional fairness, modified largest weighted delay first, and exponential rule schemes. The results show a significant improvement compared to other algorithms. This paper presents a novel technique consisting of both TOPSIS and game theory algorithms to study three smart grid applications. The novel algorithm has proven to be an effective scheduling technique for smart grid applications.

A Novel Ray-Tracing Technique for Indoor Radio Signal Prediction

The potential implementation of personal communication systems inside buildings requires a thorough understanding of signal propagation within buildings. As practical measurements are costly, difficult in accuracy, and time consuming that translate into costly service for the consumer, there is an increasing demand for efficient and accurate simulation-based indoor radio propagation prediction tools. Therefore, this study explores a new, efficient, and faster ray-tracing technique based on the Binary Angle Division algorithm for indoor radio signal prediction. The obtained simulated results are compared with other existing techniques to prove the superiority of the proposed algorithm in terms of both computational efficiency and accuracy. The results obtained from this study show satisfactory performance and good agreement with the simulated results.

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.