Abdessalem BENAMMAR

In this study we present a novel approach to estimate ultrasonic echo pattern using the two algorithms: Cuckoo Search (CS) and Adaptive Cuckoo Search (ACS). We model ultrasonic backscattered echoes in terms of superimposed Gaussian echoes corrupted by noise. Each Gaussian echo in the model is a non linear function of a set of parameters: echo bandwidth, arrival time, center frequency, amplitude and phase. The estimation of parameters is formulated as a nonlinear optimisation problem. Simulations are carried out to assess the performance of the proposed algorithms. Finally the algorithms were applied on experimental data for thickness measurement. The CS algorithm converges to best solution with less time than ACS. However, ACS algorithm outperforms CS.

Computed tomography (CT) has great impact in many fields such as medical applications, industrial inspection, etc... Low dose constraints and Limited projection are common problems in a variety of tomographic reconstruction examples which lead to wrong data. In this work, we propose a method of CT reconstruction based on the simultaneous iterative reconstruction techniques SIRT improved by imposing positivity constraint in the total variation (TVcim-p). We test our method with on Shepp-Logan phantom and different reconstruction methods. The results show that the proposed algorithm can gives images with quality comparable to other algorithms.

Computed tomography (CT) has great impact in many fields such as medical applications, industrial inspection, etc... Low dose constraints and Limited projection are common problems in a variety of tomographic reconstruction examples which lead to wrong data. In this work, we propose a method of CT reconstruction based on the simultaneous iterative reconstruction techniques SIRT improved by imposing positivity constraint in the total variation (TVcim-p). We test our method with on Shepp-Logan phantom and different reconstruction methods. The results show that the proposed algorithm can gives images with quality comparable to other algorithms.