Ayman Abubaker

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The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many(More)
This paper, presents a new component labeling algorithm which is based on scanning and labeling the objects in a single scan. The algorithm has the ability to test the four and eight connected branches of the object. This algorithm, which is fast and requires low memory allocation, can also process an image that contains large numbers of objects. The(More)
Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is(More)
Two novel threshold techniques are proposed for image segmentation which is a very critical task in any image processing. The two methods are based in scanning each image row by row and to find the proper threshold value. A modification of this method is developed to find the threshold value by average. The two methods are implemented on a mammogram and(More)
Problem Statement: Breast cancer is the second leading cause of cancer deaths in women today after lung cancer and is the most common cancer among women. The development of efficient technique to early detect the region of microcalcifications mammogram images is a must. Approach: The method proposed in this paper is to enhance the Computer Aided Diagnosis(More)
A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study. The approach taken here utilizes a hybrid neuro-fuzzy method where the neural network effectively chooses the optimum number of activation rules in order to reduce computational time for real-time(More)
This paper describes our ongoing efforts to provide efficient and accurate classification of microcalcification clusters in mammogram images. In this paper, a study of the characteristics of true microcalcifications compared to falsely detected microcalcifications is carried out using first and second order statistical texture analysis techniques. These(More)