R. M. Farouk

R. M. Farouk, M. E. Abd El-aziz, A. M. Adam

Recently, the problem of blind image separation has been widely investigated, especially the medical image denoise which is the main step in medical diag-nosis. Removing the noise without affecting relevant features of the image is the main goal. Sparse decomposition over redundant dictionaries become of the most used approaches to solve this problem. NMF codes naturally favor sparse, parts-based representations. In sparse representation, signals represented as a linear combination of a redundant dictionary atoms. In this paper, we propose an algorithm based on sparse representation over the redundant dictionary and Non-Negative Matrix Factorization (N-NMF). The algorithm initializes a dic-tionary based on training samples constructed from noised image, then it searches for the best representation for the source by using the approximate matching pursuit (AMP). The proposed N-NMF gives a better reconstruction of an image from denoised one. We have compared our numerical results with different image denoising techniques and we have found the performance of the proposed technique is promising. Keywords: Image denoising, sparse representation, dictionary learning, matching pursuit, non-negative matrix factorization.

A. M. Adam, R. M. Farouk, B. S . El-Desouky

A wide range of signs are acquired from the human body called Biomedical signs or biosignals, they can be at the cell level, organ level, or sub-atomic level. Electroencephalogramis the electrical activity from the cerebrum, the electrocardiogram is the electrical activity from the heart, electrical action from the muscle sound signals referred to as electromyogram, the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, it can help them examine and predict and cure many diseases.

R. M. Farouk, M. A. SayedElahl

One of the most challenging tasks in microarray image analysis is spot segmentation. A solution to this problem is to provide an algorithm than can be used to find any spot within the microarray image. Circular Hough Transformation (CHT) is a powerful feature extraction technique used in image analysis, computer vision, and digital image processing. CHT algorithm is applied on the cDNA microarray images to develop the accuracy and the efficiency of the spots localization, addressing and segmentation process. The purpose of the applied technique is to find imperfect instances of spots within a certain class of circles by applying a voting procedure on the cDNA microarray images for spots localization, addressing and characterizing the pixels of each spot into foreground pixels and background simultaneously. Intensive experiments on the University of North Carolina (UNC) microarray database indicate that the proposed method is superior to the K-means method and the Support vector machine (SVM). Keywords: Hough circle transformation, cDNA microarray image analysis, cDNA microarray image segmentation, spots localization and addressing, spots segmentation

R. M. Farouk, S. Badr, M. Sayed Elahl

The complementary DNA (cDNA) sequence is considered to be the magic biometric technique for personal identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN). Microarray imaging is used for the concurrent identification of thousands of genes. We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more accurate intensity measurement, leading to a more precise expression measurement of a gene. The segmented cDNA microarray image is resized and it is used as an input for the proposed artificial neural network. For matching and recognition, we have trained the artificial neural network. Recognition results are given for the galleries of cDNA sequences . The numerical results show that, the proposed matching technique is an effective in the cDNA sequences process. We also compare our results with previous results and find out that, the proposed technique is an effective matching performance.