Fath El Alem F. Ali

Two new neural network approaches to two-dimensional CT image reconstruction

Abstract Recently, we developed two neural network techniques for reconstructing two-dimensional CT images from a small number of projection data. They are simulated annealing and back propagation reconstruction techniques. The two techniques have been developed independent of each other. In this paper we present a comparative evaluation study on the two techniques. We start with introducing the two new approaches one by one, and then present simulation results. Reconstruction results by a well known conventional method — Algebraic Reconstruction Technique (ART) — is also presented for the sake of comparison. A quantitative evaluation among the three reconstruction methods is presented. A pixel-wise error estimator is used to calculate the overall error in the reconstructed images. The estimator reveals the effectiveness of the new neural network techniques compared to the conventional technique ART.

CT image reconstruction by back-propagation

A neural network model is used in CT image reconstruction from four projections. The system is based on the backpropagation algorithm for adaptation of connection weights. Satisfactory agreement between the original and reconstructed images was obtained in simulation, and the results obtained are compared to those obtained by the well-known algebraic reconstruction technique (ART), and it was found that the neural network method is more effective than ART when the number of projection directions is very limited.

Digital watermarking based on curvelet transform

This paper proposes a new watermarking method in the curvelet domain. The curvelet transform was developed in order to represent edges along curves much more efficiently than the traditional transforms. We apply the transform to watermarking and evaluate the effectiveness of the method. Our watermarking algorithm embeds a watermark in curvelet coefficients which are selected by a criterion whether they contain as much edge information as possible. We evaluated the effectiveness of the method against some watermark attacks. Experimental results show that the performance of the proposed method against most prominent attacks is good.

Evolutionary block design for video image compression

Digital video image technology has become important all the more in the past several years. In general the image space of two consecutive frames looks very similar, and thus, it is very likely that a higher compression rate is attained by using this property effectively in the video image compression technology. We propose a variable shape/size block approach to the MC (motion compensation) method, where a genetic algorithm (GA) is applied for determining optimal shapes/sizes of blocks. GAs have been used for solving many NP complete combinatorial problems, and they have provided practical and optimal solutions to them. We introduce the application specific genetic operators such as selection, crossover, or mutation. Since GAs are computation intensive, we apply the method to media such as video and CD/DVDs, rather than to the TV telephone/conference video images, which will require fast and real-time processing. We report on decoded video image quality and higher compression efficiency in comparison to existing fixed block MC methods.

CT image reconstruction by stochastic relaxation

Presented in the paper is a stochastic relaxation algorithm for reconstruction of CT image from projection data obtained from four different directions. The basic idea of the algorithm presented is similar to that of the algorithm applied by S. Geman and D. Geman (1984) to image restoration. An initial configuration of an image is generated randomly. Each pixel in the image is represented by a unit in a reconstruction in a network system. An energy function describes the current states of the system. The algorithm works to minimize the energy of the system. Dynamics of the system involve visiting each unit in the reconstruction layer individually and setting its state to a new one stochastically according to a probability distribution, determined as the sigmoid of the output from all the units.