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Articles by Ayman A. AbuBaker
Total Records ( 2 ) for Ayman A. AbuBaker
  Moussa H. Abdallah , Ayman A. AbuBaker , Rami S. Qahwaji and Mohammed H. Saleh
  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 (CAD) performance. This automatic method can detect the region of interest in mammogram image accurately and efficiently using a modified standard deviation technique. The proposed method is divided to three steps: (a) reducing the mammogram image size, (b) segmentation the breast region, and, (c) detection the region of interest. Results: The application of the technique on 386 mammogram images from the MIAS and the USF databases showed that the method is so sensitive in detecting the microcalcifications in mammogram images with 98.9% detection of true positive. Conclusions: Hence the technique proposed showed major improvement in the detection of the micro calcification and the mass region.
  Ayman A. AbuBaker , R.S .Qahwaji , Musbah J. Aqel , Hussam Al-Osta and 2Mohmmad H. Saleh
  High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a pre-processing technique for reducing the size and enhancing the quality of USF and MIAS mammogram images is introduced. The algorithm analyses the mammogram image to determine if 16-bit to 8-bit conversion process is required. Enhancement is applied later followed by a scaling process to reduce the mammogram size. The performances of the algorithms are evaluated objectively and subjectively. On average 87% reduction in size is obtained with no loss of data at the breast region.
 
 
 
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